{"id":"gen-mlp-0","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 360-feature input with 131 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-0\nComponents:\n - input (input) params={\"shape\":[1,360]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,360]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":360,"outFeatures":92}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":92,"outFeatures":968}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":968,"outFeatures":415}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":415,"outFeatures":174}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":174,"outFeatures":277}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":277,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":680591}} {"id":"gen-ae-1","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1458-dim input: encode down to a 137-dim bottleneck and decode back to 1458. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1\nComponents:\n - input (input) params={\"shape\":[1,1458]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1458]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1458,"outFeatures":751}},{"componentType":"linear","name":"enc2","params":{"inFeatures":751,"outFeatures":137}},{"componentType":"linear","name":"dec1","params":{"inFeatures":137,"outFeatures":751}},{"componentType":"linear","name":"dec2","params":{"inFeatures":751,"outFeatures":1458}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2395690}} {"id":"gen-cnn-2","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 18 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":34,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":8,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":5976}} {"id":"gen-txf-3","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 477-token sequences with 81 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3\nComponents:\n - input (input) params={\"shape\":[1,477]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,477]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14273,"embeddingDim":456}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":456,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":456,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":9040656}} {"id":"gen-gqa-4","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 427-token sequences with 33 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-4\nComponents:\n - input (input) params={\"shape\":[1,427]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,427]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9762,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":1802280}} {"id":"gen-trim-6","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2917-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-6\nComponents:\n - input (input) params={\"shape\":[1,218]}\n - fc1 (linear) params={\"inFeatures\":218,\"outFeatures\":2917}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2917,\"outFeatures\":2917}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2917,\"outFeatures\":2917}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2917,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":218,"outFeatures":126}},{"type":"update_params","name":"fc2","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"fc3","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"head","params":{"inFeatures":126,"outFeatures":45}}],"grade":{"pass":true,"score":84,"params":64890}} {"id":"gen-norm-7","family":"norm","seed":20260716,"spec":"This 129-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-7\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - fc1 (linear) params={\"inFeatures\":129,\"outFeatures\":380}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":380,\"outFeatures\":200}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":200,\"outFeatures\":292}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":292,\"outFeatures\":342}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":342,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":380}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":200}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":292}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":342}}],"grade":{"pass":true,"score":100,"params":313038}} {"id":"gen-tower-8","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 108-feature user input and a 187-feature item input, each through its own 2-layer MLP tower ending at width 439, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-8\nComponents:\n - user_input (input) params={\"shape\":[1,108]}\n - item_input (input) params={\"shape\":[1,187]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,108]}},{"componentType":"input","name":"item_input","params":{"shape":[1,187]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":108,"outFeatures":439}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":439,"outFeatures":439}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":187,"outFeatures":439}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":439,"outFeatures":439}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":878,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":515825}} {"id":"gen-grow-9","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (49-feature input, 10 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-9\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":1281}},{"type":"update_params","name":"fc2","params":{"inFeatures":1281,"outFeatures":1281}},{"type":"update_params","name":"head","params":{"inFeatures":1281,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":1716540}} {"id":"gen-mlp-10","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 367-feature input with 140 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-10\nComponents:\n - input (input) params={\"shape\":[1,367]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,367]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":367,"outFeatures":130}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":130,"outFeatures":913}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":913,"outFeatures":726}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":726,"outFeatures":389}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":389,"outFeatures":425}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":425,"outFeatures":393}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":393,"outFeatures":140}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1499022}} {"id":"gen-ae-11","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1396-dim input: encode down to a 176-dim bottleneck and decode back to 1396. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-11\nComponents:\n - input (input) params={\"shape\":[1,1396]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1396]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1396,"outFeatures":806}},{"componentType":"linear","name":"enc2","params":{"inFeatures":806,"outFeatures":176}},{"componentType":"linear","name":"dec1","params":{"inFeatures":176,"outFeatures":806}},{"componentType":"linear","name":"dec2","params":{"inFeatures":806,"outFeatures":1396}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2534064}} {"id":"gen-cnn-12","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x62x62 image with 7 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-12\nComponents:\n - input (input) params={\"shape\":[1,3,62,62]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,62,62]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":44,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":64,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":54781}} {"id":"gen-txf-13","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 101-token sequences with 10 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-13\nComponents:\n - input (input) params={\"shape\":[1,101]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,101]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21817,"embeddingDim":18}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":18,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":18,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":18,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":396774}} {"id":"gen-gqa-14","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 445-token sequences with 99 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-14\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13664,"embeddingDim":156}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":156,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[156]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":156,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[156]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":156,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[156]}},{"componentType":"linear","name":"head","params":{"inFeatures":156,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2147028}} {"id":"gen-fix-15","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (502) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-15\nComponents:\n - input (input) params={\"shape\":[1,464]}\n - embed (embedding) params={\"numEmbeddings\":49641,\"embeddingDim\":502}\n - attn (multiHeadAttention) params={\"embedDim\":502,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":502,\"outFeatures\":84}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":25969966}} {"id":"gen-trim-16","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2926-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-16\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":2926}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2926,\"outFeatures\":2926}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2926,\"outFeatures\":2926}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2926,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":41,"outFeatures":283}},{"type":"update_params","name":"fc2","params":{"inFeatures":283,"outFeatures":283}},{"type":"update_params","name":"fc3","params":{"inFeatures":283,"outFeatures":283}},{"type":"update_params","name":"head","params":{"inFeatures":283,"outFeatures":22}}],"grade":{"pass":true,"score":84,"params":178007}} {"id":"gen-norm-17","family":"norm","seed":20260716,"spec":"This 200-feature, 86-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-17\nComponents:\n - input (input) params={\"shape\":[1,200]}\n - fc1 (linear) params={\"inFeatures\":200,\"outFeatures\":386}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":386,\"outFeatures\":282}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":282,\"outFeatures\":86}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":386}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":282}}],"grade":{"pass":true,"score":94,"params":210304}} {"id":"gen-tower-18","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 139-feature user input and a 236-feature item input, each through its own 2-layer MLP tower ending at width 494, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-18\nComponents:\n - user_input (input) params={\"shape\":[1,139]}\n - item_input (input) params={\"shape\":[1,236]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,139]}},{"componentType":"input","name":"item_input","params":{"shape":[1,236]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":139,"outFeatures":494}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":494,"outFeatures":494}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":236,"outFeatures":494}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":494,"outFeatures":494}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":988,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":674310}} {"id":"gen-mlp-20","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 269-feature input with 58 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-20\nComponents:\n - input (input) params={\"shape\":[1,269]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,269]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":269,"outFeatures":699}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":699,"outFeatures":610}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":610,"outFeatures":362}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":362,"outFeatures":610}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":610,"outFeatures":431}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":431,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1343969}} {"id":"gen-ae-21","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1354-dim input: encode down to a 160-dim bottleneck and decode back to 1354. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-21\nComponents:\n - input (input) params={\"shape\":[1,1354]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1354]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1354,"outFeatures":790}},{"componentType":"linear","name":"enc2","params":{"inFeatures":790,"outFeatures":160}},{"componentType":"linear","name":"dec1","params":{"inFeatures":160,"outFeatures":790}},{"componentType":"linear","name":"dec2","params":{"inFeatures":790,"outFeatures":1354}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2392120}} {"id":"gen-cnn-22","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 23 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-22\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":24,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":62,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":45338}} {"id":"gen-txf-23","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 432-token sequences with 15 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-23\nComponents:\n - input (input) params={\"shape\":[1,432]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,432]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28261,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":116,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":116,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3441488}} {"id":"gen-gqa-24","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 229-token sequences with 41 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-24\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,229]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11849,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":951200}} {"id":"gen-fix-25","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (102) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-25\nComponents:\n - input (input) params={\"shape\":[1,383]}\n - embed (embedding) params={\"numEmbeddings\":44483,\"embeddingDim\":102}\n - attn (multiHeadAttention) params={\"embedDim\":102,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":102,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4581840}} {"id":"gen-trim-26","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3342-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-26\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - fc1 (linear) params={\"inFeatures\":125,\"outFeatures\":3342}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3342,\"outFeatures\":3342}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3342,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":125,"outFeatures":156}},{"type":"update_params","name":"fc2","params":{"inFeatures":156,"outFeatures":156}},{"type":"update_params","name":"head","params":{"inFeatures":156,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":49452}} {"id":"gen-norm-27","family":"norm","seed":20260716,"spec":"This 220-feature, 63-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-27\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":198}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":198,\"outFeatures\":502}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":502,\"outFeatures\":360}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":360,\"outFeatures\":136}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":136,\"outFeatures\":63}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":198}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":502}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":360}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":136}}],"grade":{"pass":true,"score":100,"params":381204}} {"id":"gen-tower-28","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 49-feature user input and a 83-feature item input, each through its own 2-layer MLP tower ending at width 213, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-28\nComponents:\n - user_input (input) params={\"shape\":[1,49]}\n - item_input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,49]}},{"componentType":"input","name":"item_input","params":{"shape":[1,83]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":49,"outFeatures":213}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":213,"outFeatures":213}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":83,"outFeatures":213}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":213,"outFeatures":213}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":426,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":119280}} {"id":"gen-grow-29","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (24-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-29\nComponents:\n - input (input) params={\"shape\":[1,24]}\n - fc1 (linear) params={\"inFeatures\":24,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":24,"outFeatures":870}},{"type":"update_params","name":"fc2","params":{"inFeatures":870,"outFeatures":870}},{"type":"update_params","name":"head","params":{"inFeatures":870,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":801270}} {"id":"gen-mlp-30","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 97-feature input with 160 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-30\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,97]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":97,"outFeatures":977}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":977,"outFeatures":138}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":138,"outFeatures":510}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":510,"outFeatures":160}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":381575}} {"id":"gen-ae-31","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1371-dim input: encode down to a 90-dim bottleneck and decode back to 1371. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-31\nComponents:\n - input (input) params={\"shape\":[1,1371]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1371]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1371,"outFeatures":812}},{"componentType":"linear","name":"enc2","params":{"inFeatures":812,"outFeatures":90}},{"componentType":"linear","name":"dec1","params":{"inFeatures":90,"outFeatures":812}},{"componentType":"linear","name":"dec2","params":{"inFeatures":812,"outFeatures":1371}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2372664}} {"id":"gen-cnn-32","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 3 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-32\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":20,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12081}} {"id":"gen-txf-33","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 106-token sequences with 23 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-33\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,106]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31624,"embeddingDim":112}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":112,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":112,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":112,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3694992}} {"id":"gen-gqa-34","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 448-token sequences with 81 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-34\nComponents:\n - input (input) params={\"shape\":[1,448]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,448]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39493,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5065472}} {"id":"gen-fix-35","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (456) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-35\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - embed (embedding) params={\"numEmbeddings\":30610,\"embeddingDim\":456}\n - attn (multiHeadAttention) params={\"embedDim\":456,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":456,\"outFeatures\":94}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":14832768}} {"id":"gen-trim-36","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2259-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-36\nComponents:\n - input (input) params={\"shape\":[1,152]}\n - fc1 (linear) params={\"inFeatures\":152,\"outFeatures\":2259}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2259,\"outFeatures\":2259}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2259,\"outFeatures\":2259}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2259,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":152,"outFeatures":137}},{"type":"update_params","name":"fc2","params":{"inFeatures":137,"outFeatures":137}},{"type":"update_params","name":"fc3","params":{"inFeatures":137,"outFeatures":137}},{"type":"update_params","name":"head","params":{"inFeatures":137,"outFeatures":20}}],"grade":{"pass":true,"score":84,"params":61102}} {"id":"gen-norm-37","family":"norm","seed":20260716,"spec":"This 59-feature, 97-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-37\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":450}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":450,\"outFeatures\":237}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":237,\"outFeatures\":152}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":152,\"outFeatures\":118}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":118,\"outFeatures\":97}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":450}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":237}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":152}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":118}}],"grade":{"pass":true,"score":100,"params":198606}} {"id":"gen-tower-38","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 137-feature user input and a 49-feature item input, each through its own 2-layer MLP tower ending at width 37, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-38\nComponents:\n - user_input (input) params={\"shape\":[1,137]}\n - item_input (input) params={\"shape\":[1,49]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,137]}},{"componentType":"input","name":"item_input","params":{"shape":[1,49]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":137,"outFeatures":37}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":37,"outFeatures":37}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":49,"outFeatures":37}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":37,"outFeatures":37}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":74,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":9694}} {"id":"gen-grow-39","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (128-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-39\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":128,"outFeatures":980}},{"type":"update_params","name":"fc2","params":{"inFeatures":980,"outFeatures":980}},{"type":"update_params","name":"head","params":{"inFeatures":980,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":1107400}} {"id":"gen-mlp-40","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 463-feature input with 64 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-40\nComponents:\n - input (input) params={\"shape\":[1,463]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,463]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":463,"outFeatures":452}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":452,"outFeatures":426}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":426,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":429092}} {"id":"gen-ae-41","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1973-dim input: encode down to a 222-dim bottleneck and decode back to 1973. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-41\nComponents:\n - input (input) params={\"shape\":[1,1973]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1973]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1973,"outFeatures":614}},{"componentType":"linear","name":"enc2","params":{"inFeatures":614,"outFeatures":222}},{"componentType":"linear","name":"dec1","params":{"inFeatures":222,"outFeatures":614}},{"componentType":"linear","name":"dec2","params":{"inFeatures":614,"outFeatures":1973}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2695460}} {"id":"gen-cnn-42","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 53 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-42\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":14,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46920}} {"id":"gen-txf-43","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 358-token sequences with 87 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-43\nComponents:\n - input (input) params={\"shape\":[1,358]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,358]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45415,"embeddingDim":184}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":184,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":184,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":8643216}} {"id":"gen-gqa-44","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 256-token sequences with 9 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-44\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,256]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12157,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4671744}} {"id":"gen-fix-45","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (408) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-45\nComponents:\n - input (input) params={\"shape\":[1,132]}\n - embed (embedding) params={\"numEmbeddings\":19058,\"embeddingDim\":408}\n - attn (multiHeadAttention) params={\"embedDim\":408,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":408,\"outFeatures\":77}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":8472936}} {"id":"gen-trim-46","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3785-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-46\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - fc1 (linear) params={\"inFeatures\":202,\"outFeatures\":3785}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3785,\"outFeatures\":3785}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3785,\"outFeatures\":3785}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3785,\"outFeatures\":3785}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3785,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":202,"outFeatures":128}},{"type":"update_params","name":"fc2","params":{"inFeatures":128,"outFeatures":128}},{"type":"update_params","name":"fc3","params":{"inFeatures":128,"outFeatures":128}},{"type":"update_params","name":"fc4","params":{"inFeatures":128,"outFeatures":128}},{"type":"update_params","name":"head","params":{"inFeatures":128,"outFeatures":3}}],"grade":{"pass":true,"score":88,"params":75392}} {"id":"gen-norm-47","family":"norm","seed":20260716,"spec":"This 107-feature, 34-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-47\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":415}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":415,\"outFeatures\":291}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":291,\"outFeatures\":375}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":375,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":415}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":291}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":375}}],"grade":{"pass":true,"score":100,"params":287045}} {"id":"gen-tower-48","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 218-feature user input and a 212-feature item input, each through its own 2-layer MLP tower ending at width 288, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-48\nComponents:\n - user_input (input) params={\"shape\":[1,218]}\n - item_input (input) params={\"shape\":[1,212]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,218]}},{"componentType":"input","name":"item_input","params":{"shape":[1,212]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":218,"outFeatures":288}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":212,"outFeatures":288}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":576,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":290304}} {"id":"gen-grow-49","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (49-feature input, 36 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-49\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":736}},{"type":"update_params","name":"fc2","params":{"inFeatures":736,"outFeatures":736}},{"type":"update_params","name":"head","params":{"inFeatures":736,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":604256}} {"id":"gen-mlp-50","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 433-feature input with 82 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-50\nComponents:\n - input (input) params={\"shape\":[1,433]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,433]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":433,"outFeatures":365}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":365,"outFeatures":242}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":242,"outFeatures":624}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":624,"outFeatures":774}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":774,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":943827}} {"id":"gen-ae-51","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 254-dim input: encode down to a 53-dim bottleneck and decode back to 254. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-51\nComponents:\n - input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,254]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":254,"outFeatures":768}},{"componentType":"linear","name":"enc2","params":{"inFeatures":768,"outFeatures":53}},{"componentType":"linear","name":"dec1","params":{"inFeatures":53,"outFeatures":768}},{"componentType":"linear","name":"dec2","params":{"inFeatures":768,"outFeatures":254}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":471552}} {"id":"gen-cnn-52","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 80 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-52\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":25,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15010}} {"id":"gen-txf-53","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 300-token sequences with 44 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-53\nComponents:\n - input (input) params={\"shape\":[1,300]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,300]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34357,"embeddingDim":36}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":36,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":36,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":36,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1248804}} {"id":"gen-gqa-54","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 42-token sequences with 47 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-54\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,42]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27016,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4330080}} {"id":"gen-fix-55","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (380) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-55\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - embed (embedding) params={\"numEmbeddings\":9646,\"embeddingDim\":380}\n - attn (multiHeadAttention) params={\"embedDim\":380,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":380,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":4276140}} {"id":"gen-trim-56","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3706-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-56\nComponents:\n - input (input) params={\"shape\":[1,239]}\n - fc1 (linear) params={\"inFeatures\":239,\"outFeatures\":3706}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3706,\"outFeatures\":3706}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3706,\"outFeatures\":3706}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3706,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":239,"outFeatures":208}},{"type":"update_params","name":"fc2","params":{"inFeatures":208,"outFeatures":208}},{"type":"update_params","name":"fc3","params":{"inFeatures":208,"outFeatures":208}},{"type":"update_params","name":"head","params":{"inFeatures":208,"outFeatures":6}}],"grade":{"pass":true,"score":84,"params":137488}} {"id":"gen-norm-57","family":"norm","seed":20260716,"spec":"This 185-feature, 12-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-57\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - fc1 (linear) params={\"inFeatures\":185,\"outFeatures\":469}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":469,\"outFeatures\":435}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":435,\"outFeatures\":32}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":32,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":469}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":435}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":32}}],"grade":{"pass":true,"score":100,"params":305084}} {"id":"gen-tower-58","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 61-feature user input and a 21-feature item input, each through its own 2-layer MLP tower ending at width 371, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-58\nComponents:\n - user_input (input) params={\"shape\":[1,61]}\n - item_input (input) params={\"shape\":[1,21]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,61]}},{"componentType":"input","name":"item_input","params":{"shape":[1,21]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":61,"outFeatures":371}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":371,"outFeatures":371}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":21,"outFeatures":371}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":371,"outFeatures":371}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":742,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":306446}} {"id":"gen-grow-59","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (77-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-59\nComponents:\n - input (input) params={\"shape\":[1,77]}\n - fc1 (linear) params={\"inFeatures\":77,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":77,"outFeatures":710}},{"type":"update_params","name":"fc2","params":{"inFeatures":710,"outFeatures":710}},{"type":"update_params","name":"head","params":{"inFeatures":710,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":579360}} {"id":"gen-mlp-60","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 214-feature input with 90 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-60\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,214]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":214,"outFeatures":87}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":87,"outFeatures":292}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":292,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":70302}} {"id":"gen-ae-61","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1464-dim input: encode down to a 19-dim bottleneck and decode back to 1464. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-61\nComponents:\n - input (input) params={\"shape\":[1,1464]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1464]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1464,"outFeatures":199}},{"componentType":"linear","name":"enc2","params":{"inFeatures":199,"outFeatures":19}},{"componentType":"linear","name":"dec1","params":{"inFeatures":19,"outFeatures":199}},{"componentType":"linear","name":"dec2","params":{"inFeatures":199,"outFeatures":1464}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":590234}} {"id":"gen-cnn-62","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 20 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-62\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":60,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":53,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":34216}} {"id":"gen-txf-63","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 307-token sequences with 81 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-63\nComponents:\n - input (input) params={\"shape\":[1,307]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,307]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23858,"embeddingDim":472}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":472,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":472,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":472,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":13081480}} {"id":"gen-gqa-64","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 231-token sequences with 41 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-64\nComponents:\n - input (input) params={\"shape\":[1,231]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,231]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47585,"embeddingDim":352}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":352,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[352]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":352,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[352]}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":16764352}} {"id":"gen-trim-66","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3419-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-66\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - fc1 (linear) params={\"inFeatures\":157,\"outFeatures\":3419}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3419,\"outFeatures\":3419}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3419,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":157,"outFeatures":267}},{"type":"update_params","name":"fc2","params":{"inFeatures":267,"outFeatures":267}},{"type":"update_params","name":"head","params":{"inFeatures":267,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":123087}} {"id":"gen-norm-67","family":"norm","seed":20260716,"spec":"This 120-feature, 36-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-67\nComponents:\n - input (input) params={\"shape\":[1,120]}\n - fc1 (linear) params={\"inFeatures\":120,\"outFeatures\":384}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":384,\"outFeatures\":232}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":232,\"outFeatures\":183}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":183,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":384}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":232}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":183}}],"grade":{"pass":true,"score":100,"params":184212}} {"id":"gen-tower-68","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 50-feature user input and a 25-feature item input, each through its own 2-layer MLP tower ending at width 165, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-68\nComponents:\n - user_input (input) params={\"shape\":[1,50]}\n - item_input (input) params={\"shape\":[1,25]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,50]}},{"componentType":"input","name":"item_input","params":{"shape":[1,25]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":50,"outFeatures":165}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":165,"outFeatures":165}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":25,"outFeatures":165}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":165,"outFeatures":165}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":330,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":67155}} {"id":"gen-grow-69","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (38-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-69\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":939}},{"type":"update_params","name":"fc2","params":{"inFeatures":939,"outFeatures":939}},{"type":"update_params","name":"head","params":{"inFeatures":939,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":934305}} {"id":"gen-mlp-70","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 89-feature input with 100 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-70\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,89]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":89,"outFeatures":919}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":919,"outFeatures":881}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":881,"outFeatures":664}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":664,"outFeatures":112}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1561982}} {"id":"gen-ae-71","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1854-dim input: encode down to a 195-dim bottleneck and decode back to 1854. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-71\nComponents:\n - input (input) params={\"shape\":[1,1854]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1854]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1854,"outFeatures":914}},{"componentType":"linear","name":"enc2","params":{"inFeatures":914,"outFeatures":195}},{"componentType":"linear","name":"dec1","params":{"inFeatures":195,"outFeatures":914}},{"componentType":"linear","name":"dec2","params":{"inFeatures":914,"outFeatures":1854}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3745572}} {"id":"gen-cnn-72","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 70 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-72\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":55,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":16,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36650}} {"id":"gen-txf-73","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 110-token sequences with 50 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-73\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,110]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21052,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":48,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":48,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1040544}} {"id":"gen-gqa-74","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 396-token sequences with 86 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-74\nComponents:\n - input (input) params={\"shape\":[1,396]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,396]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49491,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4759392}} {"id":"gen-trim-76","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2649-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-76\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - fc1 (linear) params={\"inFeatures\":137,\"outFeatures\":2649}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2649,\"outFeatures\":2649}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2649,\"outFeatures\":2649}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2649,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":137,"outFeatures":116}},{"type":"update_params","name":"fc2","params":{"inFeatures":116,"outFeatures":116}},{"type":"update_params","name":"fc3","params":{"inFeatures":116,"outFeatures":116}},{"type":"update_params","name":"head","params":{"inFeatures":116,"outFeatures":26}}],"grade":{"pass":true,"score":84,"params":45820}} {"id":"gen-norm-77","family":"norm","seed":20260716,"spec":"This 142-feature, 29-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-77\nComponents:\n - input (input) params={\"shape\":[1,142]}\n - fc1 (linear) params={\"inFeatures\":142,\"outFeatures\":488}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":488,\"outFeatures\":267}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":267,\"outFeatures\":476}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":476,\"outFeatures\":446}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":446,\"outFeatures\":393}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":393,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":488}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":267}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":476}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":446}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":393}}],"grade":{"pass":true,"score":100,"params":725655}} {"id":"gen-tower-78","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 108-feature user input and a 160-feature item input, each through its own 2-layer MLP tower ending at width 83, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-78\nComponents:\n - user_input (input) params={\"shape\":[1,108]}\n - item_input (input) params={\"shape\":[1,160]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,108]}},{"componentType":"input","name":"item_input","params":{"shape":[1,160]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":108,"outFeatures":83}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":83,"outFeatures":83}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":160,"outFeatures":83}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":83,"outFeatures":83}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":166,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36188}} {"id":"gen-grow-79","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (16-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-79\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":16,"outFeatures":859}},{"type":"update_params","name":"fc2","params":{"inFeatures":859,"outFeatures":859}},{"type":"update_params","name":"head","params":{"inFeatures":859,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":757638}} {"id":"gen-mlp-80","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 319-feature input with 13 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-80\nComponents:\n - input (input) params={\"shape\":[1,319]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,319]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":319,"outFeatures":736}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":736,"outFeatures":34}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":34,"outFeatures":242}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":242,"outFeatures":930}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":930,"outFeatures":124}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":124,"outFeatures":914}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":914,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":733634}} {"id":"gen-ae-81","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1699-dim input: encode down to a 33-dim bottleneck and decode back to 1699. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-81\nComponents:\n - input (input) params={\"shape\":[1,1699]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1699]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1699,"outFeatures":383}},{"componentType":"linear","name":"enc2","params":{"inFeatures":383,"outFeatures":33}},{"componentType":"linear","name":"dec1","params":{"inFeatures":33,"outFeatures":383}},{"componentType":"linear","name":"dec2","params":{"inFeatures":383,"outFeatures":1699}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1326712}} {"id":"gen-cnn-82","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 33 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-82\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":35748}} {"id":"gen-txf-83","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 236-token sequences with 21 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-83\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,236]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23156,"embeddingDim":220}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":220,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":220,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":5292540}} {"id":"gen-gqa-84","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 499-token sequences with 29 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-84\nComponents:\n - input (input) params={\"shape\":[1,499]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,499]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17015,"embeddingDim":180}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[180]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[180]}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3067920}} {"id":"gen-fix-85","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (144) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-85\nComponents:\n - input (input) params={\"shape\":[1,130]}\n - embed (embedding) params={\"numEmbeddings\":47768,\"embeddingDim\":144}\n - attn (multiHeadAttention) params={\"embedDim\":144,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":144,\"outFeatures\":82}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":6973344}} {"id":"gen-trim-86","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1864-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-86\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - fc1 (linear) params={\"inFeatures\":45,\"outFeatures\":1864}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1864,\"outFeatures\":1864}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1864,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":45,"outFeatures":312}},{"type":"update_params","name":"fc2","params":{"inFeatures":312,"outFeatures":312}},{"type":"update_params","name":"head","params":{"inFeatures":312,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":112320}} {"id":"gen-norm-87","family":"norm","seed":20260716,"spec":"This 103-feature, 2-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-87\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - fc1 (linear) params={\"inFeatures\":103,\"outFeatures\":408}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":408,\"outFeatures\":428}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":428,\"outFeatures\":345}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":345,\"outFeatures\":438}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":438,\"outFeatures\":136}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":136,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":408}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":428}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":345}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":438}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":136}}],"grade":{"pass":true,"score":100,"params":575258}} {"id":"gen-tower-88","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 60-feature user input and a 85-feature item input, each through its own 2-layer MLP tower ending at width 445, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-88\nComponents:\n - user_input (input) params={\"shape\":[1,60]}\n - item_input (input) params={\"shape\":[1,85]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,60]}},{"componentType":"input","name":"item_input","params":{"shape":[1,85]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":60,"outFeatures":445}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":445,"outFeatures":445}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":85,"outFeatures":445}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":445,"outFeatures":445}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":890,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":461465}} {"id":"gen-grow-89","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (91-feature input, 6 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-89\nComponents:\n - input (input) params={\"shape\":[1,91]}\n - fc1 (linear) params={\"inFeatures\":91,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":91,"outFeatures":1046}},{"type":"update_params","name":"fc2","params":{"inFeatures":1046,"outFeatures":1046}},{"type":"update_params","name":"head","params":{"inFeatures":1046,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":1195578}} {"id":"gen-mlp-90","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 441-feature input with 57 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-90\nComponents:\n - input (input) params={\"shape\":[1,441]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,441]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":441,"outFeatures":1007}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":1007,"outFeatures":874}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":874,"outFeatures":183}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":183,"outFeatures":367}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":367,"outFeatures":234}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":234,"outFeatures":566}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":566,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1801892}} {"id":"gen-ae-91","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 865-dim input: encode down to a 235-dim bottleneck and decode back to 865. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-91\nComponents:\n - input (input) params={\"shape\":[1,865]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,865]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":865,"outFeatures":992}},{"componentType":"linear","name":"enc2","params":{"inFeatures":992,"outFeatures":235}},{"componentType":"linear","name":"dec1","params":{"inFeatures":235,"outFeatures":992}},{"componentType":"linear","name":"dec2","params":{"inFeatures":992,"outFeatures":865}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2182400}} {"id":"gen-cnn-92","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x57x57 image with 43 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-92\nComponents:\n - input (input) params={\"shape\":[1,3,57,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,57,57]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":64,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":46,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":56,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":70006}} {"id":"gen-txf-93","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 489-token sequences with 41 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-93\nComponents:\n - input (input) params={\"shape\":[1,489]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,489]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22532,"embeddingDim":40}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":40,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":922120}} {"id":"gen-gqa-94","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 455-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-94\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,455]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19074,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4902144}} {"id":"gen-fix-95","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (392) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-95\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - embed (embedding) params={\"numEmbeddings\":12228,\"embeddingDim\":392}\n - attn (multiHeadAttention) params={\"embedDim\":392,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":392,\"outFeatures\":64}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":5433120}} {"id":"gen-trim-96","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2597-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-96\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":2597}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2597,\"outFeatures\":2597}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2597,\"outFeatures\":2597}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2597,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":83,"outFeatures":98}},{"type":"update_params","name":"fc2","params":{"inFeatures":98,"outFeatures":98}},{"type":"update_params","name":"fc3","params":{"inFeatures":98,"outFeatures":98}},{"type":"update_params","name":"head","params":{"inFeatures":98,"outFeatures":5}}],"grade":{"pass":true,"score":84,"params":27832}} {"id":"gen-norm-97","family":"norm","seed":20260716,"spec":"This 32-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-97\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":440}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":440,\"outFeatures\":65}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":65,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":440}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":65}}],"grade":{"pass":true,"score":94,"params":45150}} {"id":"gen-tower-98","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 50-feature user input and a 85-feature item input, each through its own 2-layer MLP tower ending at width 490, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-98\nComponents:\n - user_input (input) params={\"shape\":[1,50]}\n - item_input (input) params={\"shape\":[1,85]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,50]}},{"componentType":"input","name":"item_input","params":{"shape":[1,85]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":50,"outFeatures":490}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":490,"outFeatures":490}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":85,"outFeatures":490}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":490,"outFeatures":490}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":980,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":547330}} {"id":"gen-grow-99","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (127-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-99\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":127,"outFeatures":1191}},{"type":"update_params","name":"fc2","params":{"inFeatures":1191,"outFeatures":1191}},{"type":"update_params","name":"head","params":{"inFeatures":1191,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1620951}} {"id":"gen-mlp-100","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 312-feature input with 7 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-100\nComponents:\n - input (input) params={\"shape\":[1,312]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,312]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":312,"outFeatures":502}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":502,"outFeatures":942}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":942,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":636102}} {"id":"gen-ae-101","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1199-dim input: encode down to a 211-dim bottleneck and decode back to 1199. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-101\nComponents:\n - input (input) params={\"shape\":[1,1199]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1199]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1199,"outFeatures":920}},{"componentType":"linear","name":"enc2","params":{"inFeatures":920,"outFeatures":211}},{"componentType":"linear","name":"dec1","params":{"inFeatures":211,"outFeatures":920}},{"componentType":"linear","name":"dec2","params":{"inFeatures":920,"outFeatures":1199}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2594400}} {"id":"gen-cnn-102","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 43 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-102\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":20,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":63,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12213}} {"id":"gen-txf-103","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 385-token sequences with 6 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-103\nComponents:\n - input (input) params={\"shape\":[1,385]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,385]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49989,"embeddingDim":136}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":136,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":136,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":136,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":7021272}} {"id":"gen-gqa-104","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 47-token sequences with 26 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-104\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,47]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41066,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":96,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3944832}} {"id":"gen-trim-106","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2882-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-106\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - fc1 (linear) params={\"inFeatures\":213,\"outFeatures\":2882}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2882,\"outFeatures\":2882}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2882,\"outFeatures\":2882}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2882,\"outFeatures\":2882}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2882,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":213,"outFeatures":82}},{"type":"update_params","name":"fc2","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"fc3","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"fc4","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"head","params":{"inFeatures":82,"outFeatures":20}}],"grade":{"pass":true,"score":88,"params":39278}} {"id":"gen-norm-107","family":"norm","seed":20260716,"spec":"This 26-feature, 56-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-107\nComponents:\n - input (input) params={\"shape\":[1,26]}\n - fc1 (linear) params={\"inFeatures\":26,\"outFeatures\":409}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":409,\"outFeatures\":143}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":143,\"outFeatures\":56}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":143}}],"grade":{"pass":true,"score":94,"params":77129}} {"id":"gen-tower-108","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 81-feature user input and a 157-feature item input, each through its own 2-layer MLP tower ending at width 100, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-108\nComponents:\n - user_input (input) params={\"shape\":[1,81]}\n - item_input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,81]}},{"componentType":"input","name":"item_input","params":{"shape":[1,157]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":81,"outFeatures":100}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":100,"outFeatures":100}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":157,"outFeatures":100}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":100,"outFeatures":100}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":44000}} {"id":"gen-grow-109","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-109\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":1083}},{"type":"update_params","name":"fc2","params":{"inFeatures":1083,"outFeatures":1083}},{"type":"update_params","name":"head","params":{"inFeatures":1083,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1301766}} {"id":"gen-mlp-110","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 128-feature input with 108 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-110\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":128,"outFeatures":58}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":58,"outFeatures":979}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":979,"outFeatures":603}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":603,"outFeatures":108}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":719667}} {"id":"gen-ae-111","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2047-dim input: encode down to a 76-dim bottleneck and decode back to 2047. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-111\nComponents:\n - input (input) params={\"shape\":[1,2047]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2047]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2047,"outFeatures":111}},{"componentType":"linear","name":"enc2","params":{"inFeatures":111,"outFeatures":76}},{"componentType":"linear","name":"dec1","params":{"inFeatures":76,"outFeatures":111}},{"componentType":"linear","name":"dec2","params":{"inFeatures":111,"outFeatures":2047}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":471306}} {"id":"gen-cnn-112","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 23 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-112\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":33213}} {"id":"gen-txf-113","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 58-token sequences with 70 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-113\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,58]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33337,"embeddingDim":110}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":110,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3723170}} {"id":"gen-gqa-114","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 128-token sequences with 5 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-114\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13705,"embeddingDim":656}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[656]}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8993760}} {"id":"gen-trim-116","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4043-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-116\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - fc1 (linear) params={\"inFeatures\":135,\"outFeatures\":4043}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4043,\"outFeatures\":4043}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4043,\"outFeatures\":4043}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4043,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":135,"outFeatures":140}},{"type":"update_params","name":"fc2","params":{"inFeatures":140,"outFeatures":140}},{"type":"update_params","name":"fc3","params":{"inFeatures":140,"outFeatures":140}},{"type":"update_params","name":"head","params":{"inFeatures":140,"outFeatures":49}}],"grade":{"pass":true,"score":84,"params":64960}} {"id":"gen-norm-117","family":"norm","seed":20260716,"spec":"This 195-feature, 92-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-117\nComponents:\n - input (input) params={\"shape\":[1,195]}\n - fc1 (linear) params={\"inFeatures\":195,\"outFeatures\":204}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":204,\"outFeatures\":372}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":372,\"outFeatures\":423}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":423,\"outFeatures\":423}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":423,\"outFeatures\":373}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":373,\"outFeatures\":92}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":204}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":372}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":423}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":423}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":373}}],"grade":{"pass":true,"score":100,"params":644048}} {"id":"gen-tower-118","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 22-feature item input, each through its own 2-layer MLP tower ending at width 493, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-118\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,22]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,22]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":493}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":493,"outFeatures":493}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":22,"outFeatures":493}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":493,"outFeatures":493}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":986,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":616250}} {"id":"gen-grow-119","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (64-feature input, 48 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-119\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":64,"outFeatures":1011}},{"type":"update_params","name":"fc2","params":{"inFeatures":1011,"outFeatures":1011}},{"type":"update_params","name":"head","params":{"inFeatures":1011,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":1135353}} {"id":"gen-mlp-120","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 72-feature input with 48 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-120\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,72]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":72,"outFeatures":864}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":864,"outFeatures":228}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":228,"outFeatures":677}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":677,"outFeatures":171}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":171,"outFeatures":541}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":541,"outFeatures":110}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":686624}} {"id":"gen-ae-121","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 501-dim input: encode down to a 77-dim bottleneck and decode back to 501. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-121\nComponents:\n - input (input) params={\"shape\":[1,501]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,501]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":501,"outFeatures":698}},{"componentType":"linear","name":"enc2","params":{"inFeatures":698,"outFeatures":77}},{"componentType":"linear","name":"dec1","params":{"inFeatures":77,"outFeatures":698}},{"componentType":"linear","name":"dec2","params":{"inFeatures":698,"outFeatures":501}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":806888}} {"id":"gen-cnn-122","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 73 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-122\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7877}} {"id":"gen-txf-123","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 446-token sequences with 20 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-123\nComponents:\n - input (input) params={\"shape\":[1,446]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,446]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34295,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2700906}} {"id":"gen-gqa-124","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 353-token sequences with 2 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-124\nComponents:\n - input (input) params={\"shape\":[1,353]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,353]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38894,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11202048}} {"id":"gen-trim-126","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3345-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-126\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":3345}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3345,\"outFeatures\":3345}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3345,\"outFeatures\":3345}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3345,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":47,"outFeatures":231}},{"type":"update_params","name":"fc2","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"fc3","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"head","params":{"inFeatures":231,"outFeatures":35}}],"grade":{"pass":true,"score":84,"params":125664}} {"id":"gen-norm-127","family":"norm","seed":20260716,"spec":"This 105-feature, 3-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-127\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - fc1 (linear) params={\"inFeatures\":105,\"outFeatures\":105}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":105,\"outFeatures\":247}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":247,\"outFeatures\":75}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":75,\"outFeatures\":65}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":65,\"outFeatures\":102}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":102,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":105}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":247}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":75}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":65}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":102}}],"grade":{"pass":true,"score":100,"params":67296}} {"id":"gen-tower-128","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 152-feature user input and a 138-feature item input, each through its own 2-layer MLP tower ending at width 451, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-128\nComponents:\n - user_input (input) params={\"shape\":[1,152]}\n - item_input (input) params={\"shape\":[1,138]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,152]}},{"componentType":"input","name":"item_input","params":{"shape":[1,138]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":152,"outFeatures":451}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":451,"outFeatures":451}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":138,"outFeatures":451}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":451,"outFeatures":451}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":902,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":538494}} {"id":"gen-grow-129","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (126-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-129\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":126,"outFeatures":1178}},{"type":"update_params","name":"fc2","params":{"inFeatures":1178,"outFeatures":1178}},{"type":"update_params","name":"head","params":{"inFeatures":1178,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1558494}} {"id":"gen-mlp-130","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 111-feature input with 55 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-130\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,111]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":111,"outFeatures":477}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":477,"outFeatures":925}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":925,"outFeatures":193}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":193,"outFeatures":425}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":425,"outFeatures":957}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":957,"outFeatures":655}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":655,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1824307}} {"id":"gen-ae-131","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 303-dim input: encode down to a 120-dim bottleneck and decode back to 303. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-131\nComponents:\n - input (input) params={\"shape\":[1,303]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,303]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":303,"outFeatures":973}},{"componentType":"linear","name":"enc2","params":{"inFeatures":973,"outFeatures":120}},{"componentType":"linear","name":"dec1","params":{"inFeatures":120,"outFeatures":973}},{"componentType":"linear","name":"dec2","params":{"inFeatures":973,"outFeatures":303}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":823158}} {"id":"gen-cnn-132","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 32 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-132\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":38,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":49654}} {"id":"gen-txf-133","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 285-token sequences with 34 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-133\nComponents:\n - input (input) params={\"shape\":[1,285]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,285]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19088,"embeddingDim":82}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":82,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":82,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1648692}} {"id":"gen-gqa-134","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 512-token sequences with 90 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-134\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23219,"embeddingDim":352}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":352,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[352]}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":8204768}} {"id":"gen-trim-136","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2906-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-136\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - fc1 (linear) params={\"inFeatures\":240,\"outFeatures\":2906}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2906,\"outFeatures\":2906}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2906,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":240,"outFeatures":359}},{"type":"update_params","name":"fc2","params":{"inFeatures":359,"outFeatures":359}},{"type":"update_params","name":"head","params":{"inFeatures":359,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":230119}} {"id":"gen-norm-137","family":"norm","seed":20260716,"spec":"This 122-feature, 98-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-137\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":169}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":169,\"outFeatures\":200}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":200,\"outFeatures\":98}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":169}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":200}}],"grade":{"pass":true,"score":94,"params":74018}} {"id":"gen-tower-138","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 232-feature user input and a 254-feature item input, each through its own 2-layer MLP tower ending at width 307, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-138\nComponents:\n - user_input (input) params={\"shape\":[1,232]}\n - item_input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,232]}},{"componentType":"input","name":"item_input","params":{"shape":[1,254]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":232,"outFeatures":307}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":307,"outFeatures":307}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":254,"outFeatures":307}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":307,"outFeatures":307}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":614,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":338314}} {"id":"gen-grow-139","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (114-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-139\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":910}},{"type":"update_params","name":"fc2","params":{"inFeatures":910,"outFeatures":910}},{"type":"update_params","name":"head","params":{"inFeatures":910,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":951860}} {"id":"gen-mlp-140","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 453-feature input with 71 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-140\nComponents:\n - input (input) params={\"shape\":[1,453]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,453]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":453,"outFeatures":860}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":860,"outFeatures":723}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":723,"outFeatures":465}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":465,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1380570}} {"id":"gen-ae-141","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 170-dim input: encode down to a 224-dim bottleneck and decode back to 170. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-141\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,170]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":170,"outFeatures":637}},{"componentType":"linear","name":"enc2","params":{"inFeatures":637,"outFeatures":224}},{"componentType":"linear","name":"dec1","params":{"inFeatures":224,"outFeatures":637}},{"componentType":"linear","name":"dec2","params":{"inFeatures":637,"outFeatures":170}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":501956}} {"id":"gen-cnn-142","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 57 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-142\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":12639}} {"id":"gen-txf-143","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 242-token sequences with 9 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-143\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,242]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12463,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1021488}} {"id":"gen-gqa-144","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 456-token sequences with 68 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-144\nComponents:\n - input (input) params={\"shape\":[1,456]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,456]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30428,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":336,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":10246656}} {"id":"gen-trim-146","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2493-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-146\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - fc1 (linear) params={\"inFeatures\":156,\"outFeatures\":2493}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2493,\"outFeatures\":2493}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2493,\"outFeatures\":2493}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2493,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":156,"outFeatures":147}},{"type":"update_params","name":"fc2","params":{"inFeatures":147,"outFeatures":147}},{"type":"update_params","name":"fc3","params":{"inFeatures":147,"outFeatures":147}},{"type":"update_params","name":"head","params":{"inFeatures":147,"outFeatures":11}}],"grade":{"pass":true,"score":84,"params":67767}} {"id":"gen-norm-147","family":"norm","seed":20260716,"spec":"This 252-feature, 50-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-147\nComponents:\n - input (input) params={\"shape\":[1,252]}\n - fc1 (linear) params={\"inFeatures\":252,\"outFeatures\":107}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":107,\"outFeatures\":119}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":119,\"outFeatures\":360}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":360,\"outFeatures\":363}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":363,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":107}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":119}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":360}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":363}}],"grade":{"pass":true,"score":100,"params":231367}} {"id":"gen-tower-148","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 75-feature user input and a 117-feature item input, each through its own 2-layer MLP tower ending at width 260, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-148\nComponents:\n - user_input (input) params={\"shape\":[1,75]}\n - item_input (input) params={\"shape\":[1,117]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,75]}},{"componentType":"input","name":"item_input","params":{"shape":[1,117]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":75,"outFeatures":260}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":260,"outFeatures":260}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":117,"outFeatures":260}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":260,"outFeatures":260}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":520,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":185640}} {"id":"gen-grow-149","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (104-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-149\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":1377}},{"type":"update_params","name":"fc2","params":{"inFeatures":1377,"outFeatures":1377}},{"type":"update_params","name":"head","params":{"inFeatures":1377,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":2042091}} {"id":"gen-mlp-150","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 131-feature input with 93 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-150\nComponents:\n - input (input) params={\"shape\":[1,131]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,131]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":131,"outFeatures":809}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":809,"outFeatures":658}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":658,"outFeatures":520}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":520,"outFeatures":413}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":413,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1233630}} {"id":"gen-ae-151","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1622-dim input: encode down to a 8-dim bottleneck and decode back to 1622. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-151\nComponents:\n - input (input) params={\"shape\":[1,1622]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1622]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1622,"outFeatures":1022}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1022,"outFeatures":8}},{"componentType":"linear","name":"dec1","params":{"inFeatures":8,"outFeatures":1022}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1022,"outFeatures":1622}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3331720}} {"id":"gen-cnn-152","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 14 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-152\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":14}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30705}} {"id":"gen-txf-153","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 431-token sequences with 40 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-153\nComponents:\n - input (input) params={\"shape\":[1,431]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,431]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42731,"embeddingDim":126}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":126,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":126,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":126,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":126,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5579658}} {"id":"gen-gqa-154","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 141-token sequences with 81 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-154\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,141]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12162,"embeddingDim":64}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":64,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[64]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":64,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[64]}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":783552}} {"id":"gen-fix-155","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (204) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-155\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - embed (embedding) params={\"numEmbeddings\":13059,\"embeddingDim\":204}\n - attn (multiHeadAttention) params={\"embedDim\":204,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":204,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":2832744}} {"id":"gen-trim-156","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3442-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-156\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":3442}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3442,\"outFeatures\":3442}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3442,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":224,"outFeatures":71}},{"type":"update_params","name":"fc2","params":{"inFeatures":71,"outFeatures":71}},{"type":"update_params","name":"head","params":{"inFeatures":71,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":23146}} {"id":"gen-norm-157","family":"norm","seed":20260716,"spec":"This 66-feature, 54-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-157\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":93}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":93,\"outFeatures\":445}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":445,\"outFeatures\":195}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":195,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":93}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":445}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":195}}],"grade":{"pass":true,"score":100,"params":144828}} {"id":"gen-tower-158","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 160-feature user input and a 127-feature item input, each through its own 2-layer MLP tower ending at width 343, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-158\nComponents:\n - user_input (input) params={\"shape\":[1,160]}\n - item_input (input) params={\"shape\":[1,127]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,160]}},{"componentType":"input","name":"item_input","params":{"shape":[1,127]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":160,"outFeatures":343}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":343,"outFeatures":343}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":127,"outFeatures":343}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":343,"outFeatures":343}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":686,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":334425}} {"id":"gen-grow-159","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (112-feature input, 12 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-159\nComponents:\n - input (input) params={\"shape\":[1,112]}\n - fc1 (linear) params={\"inFeatures\":112,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":112,"outFeatures":914}},{"type":"update_params","name":"fc2","params":{"inFeatures":914,"outFeatures":914}},{"type":"update_params","name":"head","params":{"inFeatures":914,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":948732}} {"id":"gen-mlp-160","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 250-feature input with 51 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-160\nComponents:\n - input (input) params={\"shape\":[1,250]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,250]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":250,"outFeatures":647}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":647,"outFeatures":119}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":119,"outFeatures":537}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":537,"outFeatures":669}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":669,"outFeatures":507}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":507,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1026939}} {"id":"gen-ae-161","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1505-dim input: encode down to a 220-dim bottleneck and decode back to 1505. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-161\nComponents:\n - input (input) params={\"shape\":[1,1505]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1505]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1505,"outFeatures":272}},{"componentType":"linear","name":"enc2","params":{"inFeatures":272,"outFeatures":220}},{"componentType":"linear","name":"dec1","params":{"inFeatures":220,"outFeatures":272}},{"componentType":"linear","name":"dec2","params":{"inFeatures":272,"outFeatures":1505}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":938400}} {"id":"gen-cnn-162","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 66 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-162\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9048}} {"id":"gen-txf-163","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 108-token sequences with 82 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-163\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,108]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22730,"embeddingDim":82}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":82,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1924376}} {"id":"gen-gqa-164","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 502-token sequences with 76 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-164\nComponents:\n - input (input) params={\"shape\":[1,502]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,502]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25252,"embeddingDim":688}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":688,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[688]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":688,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[688]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":688,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[688]}},{"componentType":"linear","name":"head","params":{"inFeatures":688,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":17425664}} {"id":"gen-fix-165","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (252) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-165\nComponents:\n - input (input) params={\"shape\":[1,480]}\n - embed (embedding) params={\"numEmbeddings\":31591,\"embeddingDim\":252}\n - attn (multiHeadAttention) params={\"embedDim\":252,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":252,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":8225028}} {"id":"gen-trim-166","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1642-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-166\nComponents:\n - input (input) params={\"shape\":[1,216]}\n - fc1 (linear) params={\"inFeatures\":216,\"outFeatures\":1642}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1642,\"outFeatures\":1642}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1642,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":216,"outFeatures":200}},{"type":"update_params","name":"fc2","params":{"inFeatures":200,"outFeatures":200}},{"type":"update_params","name":"head","params":{"inFeatures":200,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":92400}} {"id":"gen-norm-167","family":"norm","seed":20260716,"spec":"This 147-feature, 21-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-167\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - fc1 (linear) params={\"inFeatures\":147,\"outFeatures\":453}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":453,\"outFeatures\":348}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":348,\"outFeatures\":46}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":46,\"outFeatures\":237}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":237,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":453}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":348}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":46}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":237}}],"grade":{"pass":true,"score":100,"params":256122}} {"id":"gen-tower-168","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 116-feature user input and a 35-feature item input, each through its own 2-layer MLP tower ending at width 339, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-168\nComponents:\n - user_input (input) params={\"shape\":[1,116]}\n - item_input (input) params={\"shape\":[1,35]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,116]}},{"componentType":"input","name":"item_input","params":{"shape":[1,35]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":116,"outFeatures":339}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":35,"outFeatures":339}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":678,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":281709}} {"id":"gen-grow-169","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (119-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-169\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - fc1 (linear) params={\"inFeatures\":119,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":119,"outFeatures":1059}},{"type":"update_params","name":"fc2","params":{"inFeatures":1059,"outFeatures":1059}},{"type":"update_params","name":"head","params":{"inFeatures":1059,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":1277154}} {"id":"gen-mlp-170","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 86-feature input with 189 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-170\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,86]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":86,"outFeatures":217}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":217,"outFeatures":731}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":731,"outFeatures":189}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":315448}} {"id":"gen-ae-171","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 199-dim input: encode down to a 191-dim bottleneck and decode back to 199. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-171\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,199]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":199,"outFeatures":581}},{"componentType":"linear","name":"enc2","params":{"inFeatures":581,"outFeatures":191}},{"componentType":"linear","name":"dec1","params":{"inFeatures":191,"outFeatures":581}},{"componentType":"linear","name":"dec2","params":{"inFeatures":581,"outFeatures":199}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":453180}} {"id":"gen-cnn-172","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 95 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-172\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":30,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":23075}} {"id":"gen-txf-173","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 159-token sequences with 28 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-173\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,159]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33501,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":88,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3012504}} {"id":"gen-gqa-174","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 434-token sequences with 28 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-174\nComponents:\n - input (input) params={\"shape\":[1,434]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,434]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47369,"embeddingDim":208}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":208,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[208]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":208,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[208]}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":9858576}} {"id":"gen-trim-176","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4082-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-176\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":4082}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4082,\"outFeatures\":4082}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4082,\"outFeatures\":4082}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4082,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":145,"outFeatures":259}},{"type":"update_params","name":"fc2","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"fc3","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"head","params":{"inFeatures":259,"outFeatures":8}}],"grade":{"pass":true,"score":84,"params":173789}} {"id":"gen-norm-177","family":"norm","seed":20260716,"spec":"This 216-feature, 54-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-177\nComponents:\n - input (input) params={\"shape\":[1,216]}\n - fc1 (linear) params={\"inFeatures\":216,\"outFeatures\":451}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":451,\"outFeatures\":159}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":159,\"outFeatures\":287}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":287,\"outFeatures\":272}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":272,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":159}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":287}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":272}}],"grade":{"pass":true,"score":100,"params":307510}} {"id":"gen-tower-178","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 63-feature user input and a 82-feature item input, each through its own 2-layer MLP tower ending at width 269, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-178\nComponents:\n - user_input (input) params={\"shape\":[1,63]}\n - item_input (input) params={\"shape\":[1,82]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,63]}},{"componentType":"input","name":"item_input","params":{"shape":[1,82]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":63,"outFeatures":269}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":269,"outFeatures":269}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":82,"outFeatures":269}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":269,"outFeatures":269}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":538,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":184265}} {"id":"gen-grow-179","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-179\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":985}},{"type":"update_params","name":"fc2","params":{"inFeatures":985,"outFeatures":985}},{"type":"update_params","name":"head","params":{"inFeatures":985,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1057890}} {"id":"gen-mlp-180","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 12-feature input with 56 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-180\nComponents:\n - input (input) params={\"shape\":[1,12]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,12]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":12,"outFeatures":73}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":73,"outFeatures":190}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":190,"outFeatures":16}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":16,"outFeatures":936}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":936,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":85178}} {"id":"gen-ae-181","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 754-dim input: encode down to a 81-dim bottleneck and decode back to 754. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-181\nComponents:\n - input (input) params={\"shape\":[1,754]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,754]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":754,"outFeatures":170}},{"componentType":"linear","name":"enc2","params":{"inFeatures":170,"outFeatures":81}},{"componentType":"linear","name":"dec1","params":{"inFeatures":81,"outFeatures":170}},{"componentType":"linear","name":"dec2","params":{"inFeatures":170,"outFeatures":754}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":283900}} {"id":"gen-cnn-182","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 50 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-182\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":46,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15971}} {"id":"gen-txf-183","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 60-token sequences with 42 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-183\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,60]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39489,"embeddingDim":280}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":280,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":280,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":280,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":11695880}} {"id":"gen-gqa-184","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 157-token sequences with 48 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-184\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,157]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27555,"embeddingDim":344}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":344,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[344]}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":9495432}} {"id":"gen-fix-185","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (208) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-185\nComponents:\n - input (input) params={\"shape\":[1,464]}\n - embed (embedding) params={\"numEmbeddings\":47880,\"embeddingDim\":208}\n - attn (multiHeadAttention) params={\"embedDim\":208,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":208,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":10138752}} {"id":"gen-trim-186","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2016-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-186\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - fc1 (linear) params={\"inFeatures\":178,\"outFeatures\":2016}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2016,\"outFeatures\":2016}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2016,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":178,"outFeatures":82}},{"type":"update_params","name":"fc2","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"head","params":{"inFeatures":82,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":22960}} {"id":"gen-norm-187","family":"norm","seed":20260716,"spec":"This 168-feature, 75-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-187\nComponents:\n - input (input) params={\"shape\":[1,168]}\n - fc1 (linear) params={\"inFeatures\":168,\"outFeatures\":225}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":225,\"outFeatures\":416}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":416,\"outFeatures\":158}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":158,\"outFeatures\":133}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":133,\"outFeatures\":293}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":293,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":225}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":416}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":158}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":133}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":293}}],"grade":{"pass":true,"score":100,"params":279086}} {"id":"gen-tower-188","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 183-feature user input and a 222-feature item input, each through its own 2-layer MLP tower ending at width 438, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-188\nComponents:\n - user_input (input) params={\"shape\":[1,183]}\n - item_input (input) params={\"shape\":[1,222]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,183]}},{"componentType":"input","name":"item_input","params":{"shape":[1,222]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":183,"outFeatures":438}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":438,"outFeatures":438}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":222,"outFeatures":438}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":438,"outFeatures":438}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":876,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":561954}} {"id":"gen-grow-189","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (51-feature input, 9 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-189\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":742}},{"type":"update_params","name":"fc2","params":{"inFeatures":742,"outFeatures":742}},{"type":"update_params","name":"head","params":{"inFeatures":742,"outFeatures":9}}],"grade":{"pass":true,"score":80,"params":595084}} {"id":"gen-mlp-190","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 338-feature input with 148 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-190\nComponents:\n - input (input) params={\"shape\":[1,338]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,338]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":338,"outFeatures":702}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":702,"outFeatures":398}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":398,"outFeatures":847}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":847,"outFeatures":148}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":979134}} {"id":"gen-ae-191","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 333-dim input: encode down to a 32-dim bottleneck and decode back to 333. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-191\nComponents:\n - input (input) params={\"shape\":[1,333]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,333]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":333,"outFeatures":475}},{"componentType":"linear","name":"enc2","params":{"inFeatures":475,"outFeatures":32}},{"componentType":"linear","name":"dec1","params":{"inFeatures":32,"outFeatures":475}},{"componentType":"linear","name":"dec2","params":{"inFeatures":475,"outFeatures":333}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":346750}} {"id":"gen-cnn-192","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 39 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-192\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":32,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":55,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":33792}} {"id":"gen-txf-193","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 251-token sequences with 100 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-193\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,251]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14481,"embeddingDim":106}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":106,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":106,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":106,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":106,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1680418}} {"id":"gen-gqa-194","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 293-token sequences with 27 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-194\nComponents:\n - input (input) params={\"shape\":[1,293]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,293]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29707,"embeddingDim":200}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[200]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[200]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[200]}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5946800}} {"id":"gen-trim-196","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3805-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-196\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - fc1 (linear) params={\"inFeatures\":228,\"outFeatures\":3805}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3805,\"outFeatures\":3805}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3805,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":228,"outFeatures":335}},{"type":"update_params","name":"fc2","params":{"inFeatures":335,"outFeatures":335}},{"type":"update_params","name":"head","params":{"inFeatures":335,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":192290}} {"id":"gen-norm-197","family":"norm","seed":20260716,"spec":"This 100-feature, 32-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-197\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - fc1 (linear) params={\"inFeatures\":100,\"outFeatures\":244}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":244,\"outFeatures\":379}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":379,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":244}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":379}}],"grade":{"pass":true,"score":94,"params":129004}} {"id":"gen-tower-198","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 167-feature user input and a 160-feature item input, each through its own 2-layer MLP tower ending at width 106, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-198\nComponents:\n - user_input (input) params={\"shape\":[1,167]}\n - item_input (input) params={\"shape\":[1,160]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,167]}},{"componentType":"input","name":"item_input","params":{"shape":[1,160]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":167,"outFeatures":106}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":106,"outFeatures":106}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":160,"outFeatures":106}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":106,"outFeatures":106}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":212,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":57346}} {"id":"gen-grow-199","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (115-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-199\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":984}},{"type":"update_params","name":"fc2","params":{"inFeatures":984,"outFeatures":984}},{"type":"update_params","name":"head","params":{"inFeatures":984,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1102080}} {"id":"gen-mlp-200","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 360-feature input with 196 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-200\nComponents:\n - input (input) params={\"shape\":[1,360]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,360]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":360,"outFeatures":99}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":99,"outFeatures":421}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":421,"outFeatures":958}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":958,"outFeatures":60}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":60,"outFeatures":137}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":137,"outFeatures":196}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":573189}} {"id":"gen-ae-201","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 289-dim input: encode down to a 204-dim bottleneck and decode back to 289. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-201\nComponents:\n - input (input) params={\"shape\":[1,289]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,289]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":289,"outFeatures":600}},{"componentType":"linear","name":"enc2","params":{"inFeatures":600,"outFeatures":204}},{"componentType":"linear","name":"dec1","params":{"inFeatures":204,"outFeatures":600}},{"componentType":"linear","name":"dec2","params":{"inFeatures":600,"outFeatures":289}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":591600}} {"id":"gen-cnn-202","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 5 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-202\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":35370}} {"id":"gen-txf-203","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 393-token sequences with 17 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-203\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11477,"embeddingDim":44}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":44,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":44,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":44,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":528968}} {"id":"gen-gqa-204","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 381-token sequences with 67 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-204\nComponents:\n - input (input) params={\"shape\":[1,381]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,381]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14836,"embeddingDim":240}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":240,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":240,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[240]}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3576720}} {"id":"gen-fix-205","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (312) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-205\nComponents:\n - input (input) params={\"shape\":[1,434]}\n - embed (embedding) params={\"numEmbeddings\":26148,\"embeddingDim\":312}\n - attn (multiHeadAttention) params={\"embedDim\":312,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":312,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":8548800}} {"id":"gen-trim-206","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3660-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-206\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":3660}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3660,\"outFeatures\":3660}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3660,\"outFeatures\":3660}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3660,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":197}},{"type":"update_params","name":"fc2","params":{"inFeatures":197,"outFeatures":197}},{"type":"update_params","name":"fc3","params":{"inFeatures":197,"outFeatures":197}},{"type":"update_params","name":"head","params":{"inFeatures":197,"outFeatures":29}}],"grade":{"pass":true,"score":84,"params":106183}} {"id":"gen-norm-207","family":"norm","seed":20260716,"spec":"This 232-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-207\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - fc1 (linear) params={\"inFeatures\":232,\"outFeatures\":472}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":472,\"outFeatures\":209}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":209,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":472}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":209}}],"grade":{"pass":true,"score":94,"params":223618}} {"id":"gen-tower-208","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 21-feature user input and a 117-feature item input, each through its own 2-layer MLP tower ending at width 433, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-208\nComponents:\n - user_input (input) params={\"shape\":[1,21]}\n - item_input (input) params={\"shape\":[1,117]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,21]}},{"componentType":"input","name":"item_input","params":{"shape":[1,117]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":21,"outFeatures":433}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":433,"outFeatures":433}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":117,"outFeatures":433}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":433,"outFeatures":433}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":866,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":435598}} {"id":"gen-grow-209","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (125-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-209\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - fc1 (linear) params={\"inFeatures\":125,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":125,"outFeatures":935}},{"type":"update_params","name":"fc2","params":{"inFeatures":935,"outFeatures":935}},{"type":"update_params","name":"head","params":{"inFeatures":935,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":1015410}} {"id":"gen-mlp-210","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 138-feature input with 45 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-210\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,138]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":138,"outFeatures":203}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":203,"outFeatures":297}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":297,"outFeatures":452}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":452,"outFeatures":887}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":887,"outFeatures":414}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":414,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1009321}} {"id":"gen-ae-211","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 122-dim input: encode down to a 71-dim bottleneck and decode back to 122. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-211\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,122]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":122,"outFeatures":826}},{"componentType":"linear","name":"enc2","params":{"inFeatures":826,"outFeatures":71}},{"componentType":"linear","name":"dec1","params":{"inFeatures":71,"outFeatures":826}},{"componentType":"linear","name":"dec2","params":{"inFeatures":826,"outFeatures":122}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":318836}} {"id":"gen-cnn-212","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 91 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-212\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":55,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":34759}} {"id":"gen-txf-213","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 275-token sequences with 37 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-213\nComponents:\n - input (input) params={\"shape\":[1,275]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,275]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42828,"embeddingDim":252}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":252,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":252,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":252,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":252,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":11564028}} {"id":"gen-gqa-214","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 115-token sequences with 97 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-214\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,115]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27394,"embeddingDim":180}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[180]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[180]}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4948380}} {"id":"gen-trim-216","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2927-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-216\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":2927}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2927,\"outFeatures\":2927}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2927,\"outFeatures\":2927}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2927,\"outFeatures\":2927}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2927,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":126,"outFeatures":298}},{"type":"update_params","name":"fc2","params":{"inFeatures":298,"outFeatures":298}},{"type":"update_params","name":"fc3","params":{"inFeatures":298,"outFeatures":298}},{"type":"update_params","name":"fc4","params":{"inFeatures":298,"outFeatures":298}},{"type":"update_params","name":"head","params":{"inFeatures":298,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":309026}} {"id":"gen-norm-217","family":"norm","seed":20260716,"spec":"This 224-feature, 89-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-217\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":285}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":285,\"outFeatures\":331}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":331,\"outFeatures\":89}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":285}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":331}}],"grade":{"pass":true,"score":94,"params":187634}} {"id":"gen-tower-218","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 137-feature user input and a 232-feature item input, each through its own 2-layer MLP tower ending at width 103, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-218\nComponents:\n - user_input (input) params={\"shape\":[1,137]}\n - item_input (input) params={\"shape\":[1,232]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,137]}},{"componentType":"input","name":"item_input","params":{"shape":[1,232]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":137,"outFeatures":103}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":103,"outFeatures":103}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":232,"outFeatures":103}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":103,"outFeatures":103}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":206,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":59431}} {"id":"gen-grow-219","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (75-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-219\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":75,"outFeatures":1314}},{"type":"update_params","name":"fc2","params":{"inFeatures":1314,"outFeatures":1314}},{"type":"update_params","name":"head","params":{"inFeatures":1314,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1850112}} {"id":"gen-mlp-220","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 113-feature input with 72 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-220\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,113]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":113,"outFeatures":549}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":549,"outFeatures":1023}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1023,"outFeatures":476}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":476,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1144884}} {"id":"gen-ae-221","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 170-dim input: encode down to a 216-dim bottleneck and decode back to 170. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-221\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,170]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":170,"outFeatures":889}},{"componentType":"linear","name":"enc2","params":{"inFeatures":889,"outFeatures":216}},{"componentType":"linear","name":"dec1","params":{"inFeatures":216,"outFeatures":889}},{"componentType":"linear","name":"dec2","params":{"inFeatures":889,"outFeatures":170}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":686308}} {"id":"gen-cnn-222","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 93 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-222\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":28,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":23,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":9,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":9981}} {"id":"gen-txf-223","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 459-token sequences with 48 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-223\nComponents:\n - input (input) params={\"shape\":[1,459]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,459]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38132,"embeddingDim":32}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":32,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":32,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1229952}} {"id":"gen-gqa-224","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 38-token sequences with 31 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-224\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,38]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24648,"embeddingDim":496}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":496,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[496]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":496,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[496]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":496,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[496]}},{"componentType":"linear","name":"head","params":{"inFeatures":496,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":12240784}} {"id":"gen-fix-225","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (512) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-225\nComponents:\n - input (input) params={\"shape\":[1,313]}\n - embed (embedding) params={\"numEmbeddings\":48083,\"embeddingDim\":512}\n - attn (multiHeadAttention) params={\"embedDim\":512,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":512,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":25676288}} {"id":"gen-trim-226","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2252-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-226\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":2252}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2252,\"outFeatures\":2252}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2252,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":321}},{"type":"update_params","name":"fc2","params":{"inFeatures":321,"outFeatures":321}},{"type":"update_params","name":"head","params":{"inFeatures":321,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":131931}} {"id":"gen-norm-227","family":"norm","seed":20260716,"spec":"This 23-feature, 21-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-227\nComponents:\n - input (input) params={\"shape\":[1,23]}\n - fc1 (linear) params={\"inFeatures\":23,\"outFeatures\":127}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":127,\"outFeatures\":409}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":409,\"outFeatures\":419}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":419,\"outFeatures\":170}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":170,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":127}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":419}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":170}}],"grade":{"pass":true,"score":100,"params":301035}} {"id":"gen-tower-228","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 220-feature user input and a 249-feature item input, each through its own 2-layer MLP tower ending at width 51, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-228\nComponents:\n - user_input (input) params={\"shape\":[1,220]}\n - item_input (input) params={\"shape\":[1,249]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,220]}},{"componentType":"input","name":"item_input","params":{"shape":[1,249]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":220,"outFeatures":51}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":249,"outFeatures":51}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":102,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29223}} {"id":"gen-grow-229","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-229\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":1205}},{"type":"update_params","name":"fc2","params":{"inFeatures":1205,"outFeatures":1205}},{"type":"update_params","name":"head","params":{"inFeatures":1205,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":1562885}} {"id":"gen-mlp-230","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 330-feature input with 57 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-230\nComponents:\n - input (input) params={\"shape\":[1,330]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,330]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":330,"outFeatures":27}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":27,"outFeatures":729}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":729,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":70146}} {"id":"gen-ae-231","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 775-dim input: encode down to a 193-dim bottleneck and decode back to 775. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-231\nComponents:\n - input (input) params={\"shape\":[1,775]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,775]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":775,"outFeatures":304}},{"componentType":"linear","name":"enc2","params":{"inFeatures":304,"outFeatures":193}},{"componentType":"linear","name":"dec1","params":{"inFeatures":193,"outFeatures":304}},{"componentType":"linear","name":"dec2","params":{"inFeatures":304,"outFeatures":775}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":588544}} {"id":"gen-cnn-232","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 97 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-232\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":21,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":38,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":36,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":23429}} {"id":"gen-txf-233","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 69-token sequences with 48 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-233\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,69]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11383,"embeddingDim":100}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":100,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1183100}} {"id":"gen-gqa-234","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 296-token sequences with 31 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-234\nComponents:\n - input (input) params={\"shape\":[1,296]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,296]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25146,"embeddingDim":152}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":152,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[152]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":152,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[152]}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3826904}} {"id":"gen-trim-236","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2625-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-236\nComponents:\n - input (input) params={\"shape\":[1,112]}\n - fc1 (linear) params={\"inFeatures\":112,\"outFeatures\":2625}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2625,\"outFeatures\":2625}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2625,\"outFeatures\":2625}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2625,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":112,"outFeatures":350}},{"type":"update_params","name":"fc2","params":{"inFeatures":350,"outFeatures":350}},{"type":"update_params","name":"fc3","params":{"inFeatures":350,"outFeatures":350}},{"type":"update_params","name":"head","params":{"inFeatures":350,"outFeatures":50}}],"grade":{"pass":true,"score":84,"params":301700}} {"id":"gen-norm-237","family":"norm","seed":20260716,"spec":"This 178-feature, 31-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-237\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - fc1 (linear) params={\"inFeatures\":178,\"outFeatures\":334}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":334,\"outFeatures\":396}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":396,\"outFeatures\":480}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":480,\"outFeatures\":152}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":152,\"outFeatures\":202}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":202,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":334}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":396}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":480}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":152}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":202}}],"grade":{"pass":true,"score":100,"params":491722}} {"id":"gen-tower-238","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 102-feature user input and a 137-feature item input, each through its own 2-layer MLP tower ending at width 478, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-238\nComponents:\n - user_input (input) params={\"shape\":[1,102]}\n - item_input (input) params={\"shape\":[1,137]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,102]}},{"componentType":"input","name":"item_input","params":{"shape":[1,137]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":102,"outFeatures":478}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":137,"outFeatures":478}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":956,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":572166}} {"id":"gen-grow-239","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (19-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-239\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":19,"outFeatures":1116}},{"type":"update_params","name":"fc2","params":{"inFeatures":1116,"outFeatures":1116}},{"type":"update_params","name":"head","params":{"inFeatures":1116,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":1288980}} {"id":"gen-mlp-240","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 323-feature input with 91 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-240\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,323]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":323,"outFeatures":47}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":47,"outFeatures":108}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":108,"outFeatures":16}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":16,"outFeatures":376}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":376,"outFeatures":391}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":391,"outFeatures":21}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":21,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":185139}} {"id":"gen-ae-241","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1664-dim input: encode down to a 221-dim bottleneck and decode back to 1664. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-241\nComponents:\n - input (input) params={\"shape\":[1,1664]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1664]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1664,"outFeatures":687}},{"componentType":"linear","name":"enc2","params":{"inFeatures":687,"outFeatures":221}},{"componentType":"linear","name":"dec1","params":{"inFeatures":221,"outFeatures":687}},{"componentType":"linear","name":"dec2","params":{"inFeatures":687,"outFeatures":1664}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2589990}} {"id":"gen-cnn-242","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 75 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-242\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":54,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":33642}} {"id":"gen-txf-243","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 345-token sequences with 2 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-243\nComponents:\n - input (input) params={\"shape\":[1,345]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,345]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16901,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2014572}} {"id":"gen-gqa-244","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 169-token sequences with 29 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-244\nComponents:\n - input (input) params={\"shape\":[1,169]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,169]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47886,"embeddingDim":116}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":116,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[116]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":116,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[116]}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5558140}} {"id":"gen-trim-246","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3889-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-246\nComponents:\n - input (input) params={\"shape\":[1,188]}\n - fc1 (linear) params={\"inFeatures\":188,\"outFeatures\":3889}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3889,\"outFeatures\":3889}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3889,\"outFeatures\":3889}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3889,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":188,"outFeatures":237}},{"type":"update_params","name":"fc2","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"fc3","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"head","params":{"inFeatures":237,"outFeatures":6}}],"grade":{"pass":true,"score":84,"params":158316}} {"id":"gen-norm-247","family":"norm","seed":20260716,"spec":"This 156-feature, 92-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-247\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - fc1 (linear) params={\"inFeatures\":156,\"outFeatures\":134}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":134,\"outFeatures\":248}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":248,\"outFeatures\":92}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":134}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":248}}],"grade":{"pass":true,"score":94,"params":76952}} {"id":"gen-tower-248","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 193-feature user input and a 145-feature item input, each through its own 2-layer MLP tower ending at width 240, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-248\nComponents:\n - user_input (input) params={\"shape\":[1,193]}\n - item_input (input) params={\"shape\":[1,145]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,193]}},{"componentType":"input","name":"item_input","params":{"shape":[1,145]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":193,"outFeatures":240}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":240,"outFeatures":240}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":145,"outFeatures":240}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":240,"outFeatures":240}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":480,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":196800}} {"id":"gen-grow-249","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (23-feature input, 36 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-249\nComponents:\n - input (input) params={\"shape\":[1,23]}\n - fc1 (linear) params={\"inFeatures\":23,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":23,"outFeatures":753}},{"type":"update_params","name":"fc2","params":{"inFeatures":753,"outFeatures":753}},{"type":"update_params","name":"head","params":{"inFeatures":753,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":611436}} {"id":"gen-mlp-250","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 321-feature input with 17 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-250\nComponents:\n - input (input) params={\"shape\":[1,321]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,321]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":321,"outFeatures":286}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":286,"outFeatures":91}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":91,"outFeatures":117}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":117,"outFeatures":959}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":959,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":256985}} {"id":"gen-ae-251","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 935-dim input: encode down to a 177-dim bottleneck and decode back to 935. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-251\nComponents:\n - input (input) params={\"shape\":[1,935]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,935]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":935,"outFeatures":441}},{"componentType":"linear","name":"enc2","params":{"inFeatures":441,"outFeatures":177}},{"componentType":"linear","name":"dec1","params":{"inFeatures":177,"outFeatures":441}},{"componentType":"linear","name":"dec2","params":{"inFeatures":441,"outFeatures":935}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":980784}} {"id":"gen-cnn-252","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x41x41 image with 25 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-252\nComponents:\n - input (input) params={\"shape\":[1,3,41,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,41,41]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":9,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":8174}} {"id":"gen-txf-253","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 147-token sequences with 94 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-253\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,147]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34855,"embeddingDim":288}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":288,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":288,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":10728864}} {"id":"gen-gqa-254","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 399-token sequences with 22 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-254\nComponents:\n - input (input) params={\"shape\":[1,399]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,399]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17264,"embeddingDim":124}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":124,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[124]}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2143464}} {"id":"gen-trim-256","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2176-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-256\nComponents:\n - input (input) params={\"shape\":[1,181]}\n - fc1 (linear) params={\"inFeatures\":181,\"outFeatures\":2176}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2176,\"outFeatures\":2176}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2176,\"outFeatures\":2176}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2176,\"outFeatures\":2176}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2176,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":181,"outFeatures":215}},{"type":"update_params","name":"fc2","params":{"inFeatures":215,"outFeatures":215}},{"type":"update_params","name":"fc3","params":{"inFeatures":215,"outFeatures":215}},{"type":"update_params","name":"fc4","params":{"inFeatures":215,"outFeatures":215}},{"type":"update_params","name":"head","params":{"inFeatures":215,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":181245}} {"id":"gen-norm-257","family":"norm","seed":20260716,"spec":"This 82-feature, 70-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-257\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":397}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":397,\"outFeatures\":277}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":277,\"outFeatures\":285}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":285,\"outFeatures\":307}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":307,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":397}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":277}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":285}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":307}}],"grade":{"pass":true,"score":100,"params":330453}} {"id":"gen-tower-258","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 109-feature user input and a 104-feature item input, each through its own 2-layer MLP tower ending at width 171, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-258\nComponents:\n - user_input (input) params={\"shape\":[1,109]}\n - item_input (input) params={\"shape\":[1,104]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,109]}},{"componentType":"input","name":"item_input","params":{"shape":[1,104]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":109,"outFeatures":171}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":171,"outFeatures":171}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":104,"outFeatures":171}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":171,"outFeatures":171}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":342,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":95247}} {"id":"gen-grow-259","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (75-feature input, 50 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-259\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":75,"outFeatures":913}},{"type":"update_params","name":"fc2","params":{"inFeatures":913,"outFeatures":913}},{"type":"update_params","name":"head","params":{"inFeatures":913,"outFeatures":50}}],"grade":{"pass":true,"score":80,"params":947694}} {"id":"gen-mlp-260","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 276-feature input with 40 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-260\nComponents:\n - input (input) params={\"shape\":[1,276]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,276]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":276,"outFeatures":432}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":432,"outFeatures":40}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":40,"outFeatures":749}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":749,"outFeatures":602}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":602,"outFeatures":229}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":229,"outFeatures":741}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":741,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":954557}} {"id":"gen-ae-261","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1051-dim input: encode down to a 97-dim bottleneck and decode back to 1051. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-261\nComponents:\n - input (input) params={\"shape\":[1,1051]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1051]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1051,"outFeatures":246}},{"componentType":"linear","name":"enc2","params":{"inFeatures":246,"outFeatures":97}},{"componentType":"linear","name":"dec1","params":{"inFeatures":97,"outFeatures":246}},{"componentType":"linear","name":"dec2","params":{"inFeatures":246,"outFeatures":1051}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":564816}} {"id":"gen-cnn-262","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 19 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-262\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":54,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":40852}} {"id":"gen-txf-263","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 156-token sequences with 44 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-263\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,156]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19170,"embeddingDim":124}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":124,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":124,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":124,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2567048}} {"id":"gen-gqa-264","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 279-token sequences with 82 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-264\nComponents:\n - input (input) params={\"shape\":[1,279]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,279]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33643,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":8633600}} {"id":"gen-fix-265","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (462) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-265\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - embed (embedding) params={\"numEmbeddings\":22504,\"embeddingDim\":462}\n - attn (multiHeadAttention) params={\"embedDim\":462,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":462,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":11253858}} {"id":"gen-trim-266","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2391-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-266\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - fc1 (linear) params={\"inFeatures\":235,\"outFeatures\":2391}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2391,\"outFeatures\":2391}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2391,\"outFeatures\":2391}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2391,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":235,"outFeatures":325}},{"type":"update_params","name":"fc2","params":{"inFeatures":325,"outFeatures":325}},{"type":"update_params","name":"fc3","params":{"inFeatures":325,"outFeatures":325}},{"type":"update_params","name":"head","params":{"inFeatures":325,"outFeatures":33}}],"grade":{"pass":true,"score":84,"params":298350}} {"id":"gen-norm-267","family":"norm","seed":20260716,"spec":"This 119-feature, 84-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-267\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - fc1 (linear) params={\"inFeatures\":119,\"outFeatures\":65}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":65,\"outFeatures\":318}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":318,\"outFeatures\":57}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":57,\"outFeatures\":182}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":182,\"outFeatures\":143}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":143,\"outFeatures\":84}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":65}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":318}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":57}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":182}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":143}}],"grade":{"pass":true,"score":100,"params":94943}} {"id":"gen-tower-268","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 146-feature user input and a 199-feature item input, each through its own 2-layer MLP tower ending at width 428, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-268\nComponents:\n - user_input (input) params={\"shape\":[1,146]}\n - item_input (input) params={\"shape\":[1,199]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,146]}},{"componentType":"input","name":"item_input","params":{"shape":[1,199]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":146,"outFeatures":428}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":428,"outFeatures":428}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":199,"outFeatures":428}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":428,"outFeatures":428}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":856,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":514884}} {"id":"gen-grow-269","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (16-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-269\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":16,"outFeatures":684}},{"type":"update_params","name":"fc2","params":{"inFeatures":684,"outFeatures":684}},{"type":"update_params","name":"head","params":{"inFeatures":684,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":482220}} {"id":"gen-mlp-270","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 13-feature input with 99 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-270\nComponents:\n - input (input) params={\"shape\":[1,13]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,13]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":13,"outFeatures":470}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":470,"outFeatures":367}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":367,"outFeatures":296}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":296,"outFeatures":1017}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":1017,"outFeatures":803}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":803,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1484412}} {"id":"gen-ae-271","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1287-dim input: encode down to a 230-dim bottleneck and decode back to 1287. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-271\nComponents:\n - input (input) params={\"shape\":[1,1287]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1287]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1287,"outFeatures":492}},{"componentType":"linear","name":"enc2","params":{"inFeatures":492,"outFeatures":230}},{"componentType":"linear","name":"dec1","params":{"inFeatures":230,"outFeatures":492}},{"componentType":"linear","name":"dec2","params":{"inFeatures":492,"outFeatures":1287}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1492728}} {"id":"gen-cnn-272","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 3 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-272\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":6729}} {"id":"gen-txf-273","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 372-token sequences with 58 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-273\nComponents:\n - input (input) params={\"shape\":[1,372]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,372]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17292,"embeddingDim":108}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":108,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":108,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1920456}} {"id":"gen-gqa-274","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 389-token sequences with 22 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-274\nComponents:\n - input (input) params={\"shape\":[1,389]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,389]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22193,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":176,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3909840}} {"id":"gen-fix-275","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (214) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-275\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - embed (embedding) params={\"numEmbeddings\":12922,\"embeddingDim\":214}\n - attn (multiHeadAttention) params={\"embedDim\":214,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":214,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2963900}} {"id":"gen-trim-276","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3789-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-276\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - fc1 (linear) params={\"inFeatures\":100,\"outFeatures\":3789}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3789,\"outFeatures\":3789}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3789,\"outFeatures\":3789}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3789,\"outFeatures\":3789}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3789,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":100,"outFeatures":173}},{"type":"update_params","name":"fc2","params":{"inFeatures":173,"outFeatures":173}},{"type":"update_params","name":"fc3","params":{"inFeatures":173,"outFeatures":173}},{"type":"update_params","name":"fc4","params":{"inFeatures":173,"outFeatures":173}},{"type":"update_params","name":"head","params":{"inFeatures":173,"outFeatures":35}}],"grade":{"pass":true,"score":88,"params":113142}} {"id":"gen-norm-277","family":"norm","seed":20260716,"spec":"This 233-feature, 84-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-277\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - fc1 (linear) params={\"inFeatures\":233,\"outFeatures\":56}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":56,\"outFeatures\":100}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":100,\"outFeatures\":84}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":56}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":100}}],"grade":{"pass":true,"score":94,"params":27048}} {"id":"gen-tower-278","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 46-feature user input and a 132-feature item input, each through its own 2-layer MLP tower ending at width 497, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-278\nComponents:\n - user_input (input) params={\"shape\":[1,46]}\n - item_input (input) params={\"shape\":[1,132]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,46]}},{"componentType":"input","name":"item_input","params":{"shape":[1,132]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":46,"outFeatures":497}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":497,"outFeatures":497}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":132,"outFeatures":497}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":497,"outFeatures":497}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":994,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":583478}} {"id":"gen-grow-279","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (93-feature input, 32 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-279\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - fc1 (linear) params={\"inFeatures\":93,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":93,"outFeatures":1263}},{"type":"update_params","name":"fc2","params":{"inFeatures":1263,"outFeatures":1263}},{"type":"update_params","name":"head","params":{"inFeatures":1263,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":1753044}} {"id":"gen-mlp-280","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 137-feature input with 54 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-280\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,137]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":137,"outFeatures":957}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":957,"outFeatures":20}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":151329}} {"id":"gen-ae-281","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 603-dim input: encode down to a 74-dim bottleneck and decode back to 603. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-281\nComponents:\n - input (input) params={\"shape\":[1,603]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,603]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":603,"outFeatures":240}},{"componentType":"linear","name":"enc2","params":{"inFeatures":240,"outFeatures":74}},{"componentType":"linear","name":"dec1","params":{"inFeatures":74,"outFeatures":240}},{"componentType":"linear","name":"dec2","params":{"inFeatures":240,"outFeatures":603}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":324960}} {"id":"gen-cnn-282","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 90 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-282\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":29,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15876}} {"id":"gen-txf-283","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 463-token sequences with 98 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-283\nComponents:\n - input (input) params={\"shape\":[1,463]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,463]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14945,"embeddingDim":108}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":108,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":108,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":108,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1717956}} {"id":"gen-gqa-284","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 462-token sequences with 10 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-284\nComponents:\n - input (input) params={\"shape\":[1,462]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,462]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39733,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":8902432}} {"id":"gen-trim-286","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2400-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-286\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":2400}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2400,\"outFeatures\":2400}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2400,\"outFeatures\":2400}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2400,\"outFeatures\":2400}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2400,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":283}},{"type":"update_params","name":"fc2","params":{"inFeatures":283,"outFeatures":283}},{"type":"update_params","name":"fc3","params":{"inFeatures":283,"outFeatures":283}},{"type":"update_params","name":"fc4","params":{"inFeatures":283,"outFeatures":283}},{"type":"update_params","name":"head","params":{"inFeatures":283,"outFeatures":23}}],"grade":{"pass":true,"score":88,"params":258662}} {"id":"gen-norm-287","family":"norm","seed":20260716,"spec":"This 180-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-287\nComponents:\n - input (input) params={\"shape\":[1,180]}\n - fc1 (linear) params={\"inFeatures\":180,\"outFeatures\":251}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":251,\"outFeatures\":139}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":139,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":251}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":139}}],"grade":{"pass":true,"score":94,"params":90911}} {"id":"gen-tower-288","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 18-feature user input and a 94-feature item input, each through its own 2-layer MLP tower ending at width 268, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-288\nComponents:\n - user_input (input) params={\"shape\":[1,18]}\n - item_input (input) params={\"shape\":[1,94]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,18]}},{"componentType":"input","name":"item_input","params":{"shape":[1,94]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":18,"outFeatures":268}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":268,"outFeatures":268}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":94,"outFeatures":268}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":268,"outFeatures":268}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":536,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":174200}} {"id":"gen-grow-289","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-289\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":1362}},{"type":"update_params","name":"fc2","params":{"inFeatures":1362,"outFeatures":1362}},{"type":"update_params","name":"head","params":{"inFeatures":1362,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":2052534}} {"id":"gen-mlp-290","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 172-feature input with 158 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-290\nComponents:\n - input (input) params={\"shape\":[1,172]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,172]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":172,"outFeatures":708}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":708,"outFeatures":841}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":841,"outFeatures":750}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":750,"outFeatures":340}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":340,"outFeatures":610}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":610,"outFeatures":177}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":177,"outFeatures":158}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1946290}} {"id":"gen-ae-291","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1299-dim input: encode down to a 159-dim bottleneck and decode back to 1299. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-291\nComponents:\n - input (input) params={\"shape\":[1,1299]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1299]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1299,"outFeatures":80}},{"componentType":"linear","name":"enc2","params":{"inFeatures":80,"outFeatures":159}},{"componentType":"linear","name":"dec1","params":{"inFeatures":159,"outFeatures":80}},{"componentType":"linear","name":"dec2","params":{"inFeatures":80,"outFeatures":1299}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":233280}} {"id":"gen-cnn-292","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 86 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-292\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":11,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":19,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":6290}} {"id":"gen-txf-293","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 481-token sequences with 2 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-293\nComponents:\n - input (input) params={\"shape\":[1,481]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,481]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31903,"embeddingDim":312}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":312,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":312,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":312,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":10733112}} {"id":"gen-gqa-294","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 287-token sequences with 17 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-294\nComponents:\n - input (input) params={\"shape\":[1,287]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,287]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36559,"embeddingDim":640}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":640,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":640,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":640,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[640]}},{"componentType":"linear","name":"head","params":{"inFeatures":640,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":23408640}} {"id":"gen-fix-295","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (134) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-295\nComponents:\n - input (input) params={\"shape\":[1,408]}\n - embed (embedding) params={\"numEmbeddings\":34651,\"embeddingDim\":134}\n - attn (multiHeadAttention) params={\"embedDim\":134,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":134,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4716264}} {"id":"gen-trim-296","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3442-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-296\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":3442}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3442,\"outFeatures\":3442}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3442,\"outFeatures\":3442}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3442,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":59,"outFeatures":82}},{"type":"update_params","name":"fc2","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"fc3","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"head","params":{"inFeatures":82,"outFeatures":43}}],"grade":{"pass":true,"score":84,"params":21812}} {"id":"gen-norm-297","family":"norm","seed":20260716,"spec":"This 30-feature, 86-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-297\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":409}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":409,\"outFeatures\":103}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":103,\"outFeatures\":82}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":82,\"outFeatures\":86}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":103}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":82}}],"grade":{"pass":true,"score":100,"params":69895}} {"id":"gen-tower-298","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 83-feature user input and a 168-feature item input, each through its own 2-layer MLP tower ending at width 167, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-298\nComponents:\n - user_input (input) params={\"shape\":[1,83]}\n - item_input (input) params={\"shape\":[1,168]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,83]}},{"componentType":"input","name":"item_input","params":{"shape":[1,168]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":83,"outFeatures":167}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":167,"outFeatures":167}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":168,"outFeatures":167}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":167,"outFeatures":167}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":334,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":98029}} {"id":"gen-grow-299","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (34-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-299\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - fc1 (linear) params={\"inFeatures\":34,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":34,"outFeatures":874}},{"type":"update_params","name":"fc2","params":{"inFeatures":874,"outFeatures":874}},{"type":"update_params","name":"head","params":{"inFeatures":874,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":806702}} {"id":"gen-mlp-300","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 49-feature input with 93 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-300\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,49]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":49,"outFeatures":790}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":790,"outFeatures":814}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":814,"outFeatures":207}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":207,"outFeatures":422}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":422,"outFeatures":396}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":396,"outFeatures":376}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1288598}} {"id":"gen-ae-301","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 640-dim input: encode down to a 27-dim bottleneck and decode back to 640. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-301\nComponents:\n - input (input) params={\"shape\":[1,640]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,640]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":640,"outFeatures":122}},{"componentType":"linear","name":"enc2","params":{"inFeatures":122,"outFeatures":27}},{"componentType":"linear","name":"dec1","params":{"inFeatures":27,"outFeatures":122}},{"componentType":"linear","name":"dec2","params":{"inFeatures":122,"outFeatures":640}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":162748}} {"id":"gen-cnn-302","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 33 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-302\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4521}} {"id":"gen-txf-303","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 475-token sequences with 29 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-303\nComponents:\n - input (input) params={\"shape\":[1,475]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,475]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13075,"embeddingDim":464}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":464,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":464,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6941440}} {"id":"gen-gqa-304","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 171-token sequences with 97 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-304\nComponents:\n - input (input) params={\"shape\":[1,171]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,171]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28445,"embeddingDim":180}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[180]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[180]}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5137560}} {"id":"gen-fix-305","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (320) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-305\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - embed (embedding) params={\"numEmbeddings\":47465,\"embeddingDim\":320}\n - attn (multiHeadAttention) params={\"embedDim\":320,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":320,\"outFeatures\":65}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":15619200}} {"id":"gen-trim-306","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4070-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-306\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - fc1 (linear) params={\"inFeatures\":227,\"outFeatures\":4070}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4070,\"outFeatures\":4070}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4070,\"outFeatures\":4070}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4070,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":227,"outFeatures":373}},{"type":"update_params","name":"fc2","params":{"inFeatures":373,"outFeatures":373}},{"type":"update_params","name":"fc3","params":{"inFeatures":373,"outFeatures":373}},{"type":"update_params","name":"head","params":{"inFeatures":373,"outFeatures":28}}],"grade":{"pass":true,"score":84,"params":373373}} {"id":"gen-norm-307","family":"norm","seed":20260716,"spec":"This 61-feature, 99-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-307\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - fc1 (linear) params={\"inFeatures\":61,\"outFeatures\":230}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":230,\"outFeatures\":141}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":141,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":230}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":141}}],"grade":{"pass":true,"score":94,"params":60419}} {"id":"gen-tower-308","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 99-feature user input and a 69-feature item input, each through its own 2-layer MLP tower ending at width 210, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-308\nComponents:\n - user_input (input) params={\"shape\":[1,99]}\n - item_input (input) params={\"shape\":[1,69]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,99]}},{"componentType":"input","name":"item_input","params":{"shape":[1,69]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":99,"outFeatures":210}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":210,"outFeatures":210}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":69,"outFeatures":210}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":210,"outFeatures":210}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":420,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":123900}} {"id":"gen-grow-309","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (110-feature input, 9 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-309\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":110,"outFeatures":853}},{"type":"update_params","name":"fc2","params":{"inFeatures":853,"outFeatures":853}},{"type":"update_params","name":"head","params":{"inFeatures":853,"outFeatures":9}}],"grade":{"pass":true,"score":80,"params":829116}} {"id":"gen-mlp-310","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 129-feature input with 6 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-310\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,129]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":129,"outFeatures":104}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":104,"outFeatures":989}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":989,"outFeatures":631}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":631,"outFeatures":411}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":411,"outFeatures":132}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":132,"outFeatures":812}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":812,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1165980}} {"id":"gen-ae-311","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1673-dim input: encode down to a 80-dim bottleneck and decode back to 1673. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-311\nComponents:\n - input (input) params={\"shape\":[1,1673]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1673]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1673,"outFeatures":578}},{"componentType":"linear","name":"enc2","params":{"inFeatures":578,"outFeatures":80}},{"componentType":"linear","name":"dec1","params":{"inFeatures":80,"outFeatures":578}},{"componentType":"linear","name":"dec2","params":{"inFeatures":578,"outFeatures":1673}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2026468}} {"id":"gen-cnn-312","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 49 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-312\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":49,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":35413}} {"id":"gen-txf-313","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 310-token sequences with 76 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-313\nComponents:\n - input (input) params={\"shape\":[1,310]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,310]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42762,"embeddingDim":172}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":172,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":172,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7604808}} {"id":"gen-gqa-314","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 111-token sequences with 64 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-314\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,111]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13266,"embeddingDim":132}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":132,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[132]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":132,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[132]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":132,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[132]}},{"componentType":"linear","name":"head","params":{"inFeatures":132,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1759560}} {"id":"gen-trim-316","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3735-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-316\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - fc1 (linear) params={\"inFeatures\":221,\"outFeatures\":3735}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3735,\"outFeatures\":3735}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3735,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":221,"outFeatures":347}},{"type":"update_params","name":"fc2","params":{"inFeatures":347,"outFeatures":347}},{"type":"update_params","name":"head","params":{"inFeatures":347,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":209588}} {"id":"gen-norm-317","family":"norm","seed":20260716,"spec":"This 19-feature, 75-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-317\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":249}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":249,\"outFeatures\":98}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":98,\"outFeatures\":113}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":113,\"outFeatures\":296}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":296,\"outFeatures\":95}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":95,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":249}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":98}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":113}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":296}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":95}}],"grade":{"pass":true,"score":100,"params":108900}} {"id":"gen-tower-318","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 214-feature user input and a 207-feature item input, each through its own 2-layer MLP tower ending at width 339, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-318\nComponents:\n - user_input (input) params={\"shape\":[1,214]}\n - item_input (input) params={\"shape\":[1,207]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,214]}},{"componentType":"input","name":"item_input","params":{"shape":[1,207]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":214,"outFeatures":339}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":207,"outFeatures":339}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":678,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":373239}} {"id":"gen-grow-319","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (30-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-319\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":30,"outFeatures":1113}},{"type":"update_params","name":"fc2","params":{"inFeatures":1113,"outFeatures":1113}},{"type":"update_params","name":"head","params":{"inFeatures":1113,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":1304436}} {"id":"gen-mlp-320","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 114-feature input with 156 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-320\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,114]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":114,"outFeatures":308}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":308,"outFeatures":158}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":158,"outFeatures":912}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":912,"outFeatures":685}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":685,"outFeatures":156}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":959452}} {"id":"gen-ae-321","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1250-dim input: encode down to a 91-dim bottleneck and decode back to 1250. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-321\nComponents:\n - input (input) params={\"shape\":[1,1250]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1250]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1250,"outFeatures":219}},{"componentType":"linear","name":"enc2","params":{"inFeatures":219,"outFeatures":91}},{"componentType":"linear","name":"dec1","params":{"inFeatures":91,"outFeatures":219}},{"componentType":"linear","name":"dec2","params":{"inFeatures":219,"outFeatures":1250}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":587358}} {"id":"gen-cnn-322","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 19 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-322\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15141}} {"id":"gen-txf-323","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 349-token sequences with 21 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-323\nComponents:\n - input (input) params={\"shape\":[1,349]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,349]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11742,"embeddingDim":440}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":440,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":440,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":5950120}} {"id":"gen-gqa-324","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 202-token sequences with 22 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-324\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,202]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45612,"embeddingDim":40}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[40]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[40]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[40]}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1825360}} {"id":"gen-trim-326","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3619-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-326\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - fc1 (linear) params={\"inFeatures\":69,\"outFeatures\":3619}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3619,\"outFeatures\":3619}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3619,\"outFeatures\":3619}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3619,\"outFeatures\":3619}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3619,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":69,"outFeatures":229}},{"type":"update_params","name":"fc2","params":{"inFeatures":229,"outFeatures":229}},{"type":"update_params","name":"fc3","params":{"inFeatures":229,"outFeatures":229}},{"type":"update_params","name":"fc4","params":{"inFeatures":229,"outFeatures":229}},{"type":"update_params","name":"head","params":{"inFeatures":229,"outFeatures":43}}],"grade":{"pass":true,"score":88,"params":182971}} {"id":"gen-norm-327","family":"norm","seed":20260716,"spec":"This 110-feature, 57-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-327\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":169}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":169,\"outFeatures\":451}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":451,\"outFeatures\":407}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":407,\"outFeatures\":231}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":231,\"outFeatures\":484}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":484,\"outFeatures\":57}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":169}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":407}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":231}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":484}}],"grade":{"pass":true,"score":100,"params":511775}} {"id":"gen-tower-328","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 99-feature user input and a 183-feature item input, each through its own 2-layer MLP tower ending at width 263, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-328\nComponents:\n - user_input (input) params={\"shape\":[1,99]}\n - item_input (input) params={\"shape\":[1,183]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,99]}},{"componentType":"input","name":"item_input","params":{"shape":[1,183]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":99,"outFeatures":263}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":263,"outFeatures":263}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":183,"outFeatures":263}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":263,"outFeatures":263}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":526,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":213030}} {"id":"gen-grow-329","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (27-feature input, 34 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-329\nComponents:\n - input (input) params={\"shape\":[1,27]}\n - fc1 (linear) params={\"inFeatures\":27,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":27,"outFeatures":1287}},{"type":"update_params","name":"fc2","params":{"inFeatures":1287,"outFeatures":1287}},{"type":"update_params","name":"head","params":{"inFeatures":1287,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":1734876}} {"id":"gen-mlp-330","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 256-feature input with 177 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-330\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,256]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":256,"outFeatures":330}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":330,"outFeatures":929}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":929,"outFeatures":993}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":993,"outFeatures":181}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":181,"outFeatures":106}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":106,"outFeatures":224}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":177}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1575858}} {"id":"gen-ae-331","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 900-dim input: encode down to a 217-dim bottleneck and decode back to 900. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-331\nComponents:\n - input (input) params={\"shape\":[1,900]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,900]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":900,"outFeatures":761}},{"componentType":"linear","name":"enc2","params":{"inFeatures":761,"outFeatures":217}},{"componentType":"linear","name":"dec1","params":{"inFeatures":217,"outFeatures":761}},{"componentType":"linear","name":"dec2","params":{"inFeatures":761,"outFeatures":900}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1700074}} {"id":"gen-cnn-332","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 26 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-332\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":16139}} {"id":"gen-txf-333","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 357-token sequences with 86 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-333\nComponents:\n - input (input) params={\"shape\":[1,357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,357]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22327,"embeddingDim":94}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":94,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":94,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":94,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":94,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2212854}} {"id":"gen-gqa-334","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 46-token sequences with 91 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-334\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,46]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46865,"embeddingDim":92}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[92]}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4319952}} {"id":"gen-fix-335","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (136) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-335\nComponents:\n - input (input) params={\"shape\":[1,246]}\n - embed (embedding) params={\"numEmbeddings\":32671,\"embeddingDim\":136}\n - attn (multiHeadAttention) params={\"embedDim\":136,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":136,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":4518192}} {"id":"gen-trim-336","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1536-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-336\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":1536}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1536,\"outFeatures\":1536}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1536,\"outFeatures\":1536}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1536,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":219}},{"type":"update_params","name":"fc2","params":{"inFeatures":219,"outFeatures":219}},{"type":"update_params","name":"fc3","params":{"inFeatures":219,"outFeatures":219}},{"type":"update_params","name":"head","params":{"inFeatures":219,"outFeatures":37}}],"grade":{"pass":true,"score":84,"params":114756}} {"id":"gen-norm-337","family":"norm","seed":20260716,"spec":"This 99-feature, 13-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-337\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":454}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":454,\"outFeatures\":79}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":79,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":454}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":79}}],"grade":{"pass":true,"score":94,"params":81839}} {"id":"gen-tower-338","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 151-feature user input and a 29-feature item input, each through its own 2-layer MLP tower ending at width 427, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-338\nComponents:\n - user_input (input) params={\"shape\":[1,151]}\n - item_input (input) params={\"shape\":[1,29]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,151]}},{"componentType":"input","name":"item_input","params":{"shape":[1,29]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":151,"outFeatures":427}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":427,"outFeatures":427}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":29,"outFeatures":427}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":427,"outFeatures":427}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":854,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":442372}} {"id":"gen-grow-339","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (96-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-339\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":96,"outFeatures":1386}},{"type":"update_params","name":"fc2","params":{"inFeatures":1386,"outFeatures":1386}},{"type":"update_params","name":"head","params":{"inFeatures":1386,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":2080386}} {"id":"gen-mlp-340","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 354-feature input with 168 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-340\nComponents:\n - input (input) params={\"shape\":[1,354]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,354]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":354,"outFeatures":145}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":145,"outFeatures":593}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":593,"outFeatures":90}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":90,"outFeatures":599}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":599,"outFeatures":185}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":185,"outFeatures":308}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":308,"outFeatures":168}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":464134}} {"id":"gen-ae-341","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 813-dim input: encode down to a 200-dim bottleneck and decode back to 813. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-341\nComponents:\n - input (input) params={\"shape\":[1,813]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,813]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":813,"outFeatures":339}},{"componentType":"linear","name":"enc2","params":{"inFeatures":339,"outFeatures":200}},{"componentType":"linear","name":"dec1","params":{"inFeatures":200,"outFeatures":339}},{"componentType":"linear","name":"dec2","params":{"inFeatures":339,"outFeatures":813}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":686814}} {"id":"gen-cnn-342","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 93 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-342\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":49,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":51855}} {"id":"gen-txf-343","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 177-token sequences with 32 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-343\nComponents:\n - input (input) params={\"shape\":[1,177]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,177]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41018,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":120,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5041200}} {"id":"gen-gqa-344","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 380-token sequences with 53 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-344\nComponents:\n - input (input) params={\"shape\":[1,380]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,380]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45240,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":17392512}} {"id":"gen-trim-346","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1687-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-346\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":1687}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1687,\"outFeatures\":1687}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1687,\"outFeatures\":1687}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1687,\"outFeatures\":1687}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1687,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":366}},{"type":"update_params","name":"fc2","params":{"inFeatures":366,"outFeatures":366}},{"type":"update_params","name":"fc3","params":{"inFeatures":366,"outFeatures":366}},{"type":"update_params","name":"fc4","params":{"inFeatures":366,"outFeatures":366}},{"type":"update_params","name":"head","params":{"inFeatures":366,"outFeatures":16}}],"grade":{"pass":true,"score":88,"params":449814}} {"id":"gen-norm-347","family":"norm","seed":20260716,"spec":"This 197-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-347\nComponents:\n - input (input) params={\"shape\":[1,197]}\n - fc1 (linear) params={\"inFeatures\":197,\"outFeatures\":116}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":116,\"outFeatures\":220}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":220,\"outFeatures\":489}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":489,\"outFeatures\":100}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":100,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":116}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":220}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":489}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":100}}],"grade":{"pass":true,"score":100,"params":210852}} {"id":"gen-tower-348","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 43-feature user input and a 98-feature item input, each through its own 2-layer MLP tower ending at width 127, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-348\nComponents:\n - user_input (input) params={\"shape\":[1,43]}\n - item_input (input) params={\"shape\":[1,98]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,43]}},{"componentType":"input","name":"item_input","params":{"shape":[1,98]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":43,"outFeatures":127}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":127,"outFeatures":127}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":98,"outFeatures":127}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":127,"outFeatures":127}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":254,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":50419}} {"id":"gen-grow-349","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (23-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-349\nComponents:\n - input (input) params={\"shape\":[1,23]}\n - fc1 (linear) params={\"inFeatures\":23,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":23,"outFeatures":926}},{"type":"update_params","name":"fc2","params":{"inFeatures":926,"outFeatures":926}},{"type":"update_params","name":"head","params":{"inFeatures":926,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":882478}} {"id":"gen-mlp-350","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 28-feature input with 156 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-350\nComponents:\n - input (input) params={\"shape\":[1,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,28]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":28,"outFeatures":478}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":478,"outFeatures":418}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":418,"outFeatures":952}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":952,"outFeatures":462}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":462,"outFeatures":156}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1123020}} {"id":"gen-ae-351","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 213-dim input: encode down to a 194-dim bottleneck and decode back to 213. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-351\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,213]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":213,"outFeatures":513}},{"componentType":"linear","name":"enc2","params":{"inFeatures":513,"outFeatures":194}},{"componentType":"linear","name":"dec1","params":{"inFeatures":194,"outFeatures":513}},{"componentType":"linear","name":"dec2","params":{"inFeatures":513,"outFeatures":213}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":417582}} {"id":"gen-cnn-352","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 79 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-352\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":34,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":21904}} {"id":"gen-txf-353","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 232-token sequences with 77 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-353\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,232]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39133,"embeddingDim":44}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":44,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":44,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1740728}} {"id":"gen-gqa-354","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 283-token sequences with 96 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-354\nComponents:\n - input (input) params={\"shape\":[1,283]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,283]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25062,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6842976}} {"id":"gen-fix-355","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (404) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-355\nComponents:\n - input (input) params={\"shape\":[1,162]}\n - embed (embedding) params={\"numEmbeddings\":40346,\"embeddingDim\":404}\n - attn (multiHeadAttention) params={\"embedDim\":404,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":404,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":16982948}} {"id":"gen-trim-356","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2192-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-356\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":2192}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2192,\"outFeatures\":2192}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2192,\"outFeatures\":2192}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2192,\"outFeatures\":2192}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2192,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":64}},{"type":"update_params","name":"fc2","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"fc3","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"fc4","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"head","params":{"inFeatures":64,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":18944}} {"id":"gen-norm-357","family":"norm","seed":20260716,"spec":"This 219-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-357\nComponents:\n - input (input) params={\"shape\":[1,219]}\n - fc1 (linear) params={\"inFeatures\":219,\"outFeatures\":364}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":364,\"outFeatures\":229}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":229,\"outFeatures\":71}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":71,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":364}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":229}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":71}}],"grade":{"pass":true,"score":100,"params":182313}} {"id":"gen-tower-358","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 242-feature user input and a 23-feature item input, each through its own 2-layer MLP tower ending at width 291, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-358\nComponents:\n - user_input (input) params={\"shape\":[1,242]}\n - item_input (input) params={\"shape\":[1,23]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,242]}},{"componentType":"input","name":"item_input","params":{"shape":[1,23]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":242,"outFeatures":291}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":23,"outFeatures":291}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":582,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":247059}} {"id":"gen-grow-359","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (60-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-359\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":1123}},{"type":"update_params","name":"fc2","params":{"inFeatures":1123,"outFeatures":1123}},{"type":"update_params","name":"head","params":{"inFeatures":1123,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":1355461}} {"id":"gen-mlp-360","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 279-feature input with 23 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-360\nComponents:\n - input (input) params={\"shape\":[1,279]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,279]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":279,"outFeatures":118}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":118,"outFeatures":609}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":609,"outFeatures":866}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":866,"outFeatures":557}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":557,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1127351}} {"id":"gen-ae-361","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1064-dim input: encode down to a 119-dim bottleneck and decode back to 1064. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-361\nComponents:\n - input (input) params={\"shape\":[1,1064]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1064]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1064,"outFeatures":861}},{"componentType":"linear","name":"enc2","params":{"inFeatures":861,"outFeatures":119}},{"componentType":"linear","name":"dec1","params":{"inFeatures":119,"outFeatures":861}},{"componentType":"linear","name":"dec2","params":{"inFeatures":861,"outFeatures":1064}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2037126}} {"id":"gen-cnn-362","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 57 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-362\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":50,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29502}} {"id":"gen-txf-363","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 210-token sequences with 41 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-363\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,210]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9953,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":80,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":876320}} {"id":"gen-gqa-364","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 304-token sequences with 21 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-364\nComponents:\n - input (input) params={\"shape\":[1,304]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,304]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21145,"embeddingDim":704}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":704,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[704]}},{"componentType":"linear","name":"head","params":{"inFeatures":704,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":14900864}} {"id":"gen-trim-366","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2267-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-366\nComponents:\n - input (input) params={\"shape\":[1,182]}\n - fc1 (linear) params={\"inFeatures\":182,\"outFeatures\":2267}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2267,\"outFeatures\":2267}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2267,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":182,"outFeatures":255}},{"type":"update_params","name":"fc2","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"head","params":{"inFeatures":255,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":123165}} {"id":"gen-norm-367","family":"norm","seed":20260716,"spec":"This 67-feature, 14-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-367\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":371}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":371,\"outFeatures\":424}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":424,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":371}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":424}}],"grade":{"pass":true,"score":94,"params":188097}} {"id":"gen-tower-368","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 250-feature user input and a 47-feature item input, each through its own 2-layer MLP tower ending at width 200, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-368\nComponents:\n - user_input (input) params={\"shape\":[1,250]}\n - item_input (input) params={\"shape\":[1,47]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,250]}},{"componentType":"input","name":"item_input","params":{"shape":[1,47]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":250,"outFeatures":200}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":200,"outFeatures":200}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":47,"outFeatures":200}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":200,"outFeatures":200}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":139800}} {"id":"gen-grow-369","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (113-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-369\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - fc1 (linear) params={\"inFeatures\":113,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":113,"outFeatures":921}},{"type":"update_params","name":"fc2","params":{"inFeatures":921,"outFeatures":921}},{"type":"update_params","name":"head","params":{"inFeatures":921,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":997443}} {"id":"gen-mlp-370","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 243-feature input with 152 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-370\nComponents:\n - input (input) params={\"shape\":[1,243]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,243]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":243,"outFeatures":225}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":225,"outFeatures":149}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":149,"outFeatures":534}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":534,"outFeatures":458}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":458,"outFeatures":152}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":481954}} {"id":"gen-ae-371","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 939-dim input: encode down to a 214-dim bottleneck and decode back to 939. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-371\nComponents:\n - input (input) params={\"shape\":[1,939]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,939]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":939,"outFeatures":80}},{"componentType":"linear","name":"enc2","params":{"inFeatures":80,"outFeatures":214}},{"componentType":"linear","name":"dec1","params":{"inFeatures":214,"outFeatures":80}},{"componentType":"linear","name":"dec2","params":{"inFeatures":80,"outFeatures":939}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":184480}} {"id":"gen-cnn-372","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 23 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-372\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":19,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":15,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":13921}} {"id":"gen-txf-373","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 384-token sequences with 8 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-373\nComponents:\n - input (input) params={\"shape\":[1,384]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,384]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26804,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":200,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5842400}} {"id":"gen-gqa-374","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 297-token sequences with 19 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-374\nComponents:\n - input (input) params={\"shape\":[1,297]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,297]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8407,"embeddingDim":432}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":432,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[432]}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3640032}} {"id":"gen-trim-376","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3783-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-376\nComponents:\n - input (input) params={\"shape\":[1,172]}\n - fc1 (linear) params={\"inFeatures\":172,\"outFeatures\":3783}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3783,\"outFeatures\":3783}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3783,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":172,"outFeatures":120}},{"type":"update_params","name":"fc2","params":{"inFeatures":120,"outFeatures":120}},{"type":"update_params","name":"head","params":{"inFeatures":120,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":35760}} {"id":"gen-norm-377","family":"norm","seed":20260716,"spec":"This 150-feature, 2-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-377\nComponents:\n - input (input) params={\"shape\":[1,150]}\n - fc1 (linear) params={\"inFeatures\":150,\"outFeatures\":152}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":152,\"outFeatures\":374}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":374,\"outFeatures\":275}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":275,\"outFeatures\":145}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":145,\"outFeatures\":82}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":82,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":152}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":374}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":275}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":145}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":82}}],"grade":{"pass":true,"score":100,"params":234427}} {"id":"gen-tower-378","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 20-feature user input and a 16-feature item input, each through its own 2-layer MLP tower ending at width 502, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-378\nComponents:\n - user_input (input) params={\"shape\":[1,20]}\n - item_input (input) params={\"shape\":[1,16]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,20]}},{"componentType":"input","name":"item_input","params":{"shape":[1,16]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":20,"outFeatures":502}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":502,"outFeatures":502}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":16,"outFeatures":502}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":502,"outFeatures":502}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1004,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":523084}} {"id":"gen-grow-379","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (68-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-379\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":68,"outFeatures":1070}},{"type":"update_params","name":"fc2","params":{"inFeatures":1070,"outFeatures":1070}},{"type":"update_params","name":"head","params":{"inFeatures":1070,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1221940}} {"id":"gen-mlp-380","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 325-feature input with 101 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-380\nComponents:\n - input (input) params={\"shape\":[1,325]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,325]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":325,"outFeatures":700}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":700,"outFeatures":646}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":646,"outFeatures":274}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":274,"outFeatures":923}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":923,"outFeatures":101}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1202829}} {"id":"gen-ae-381","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1425-dim input: encode down to a 211-dim bottleneck and decode back to 1425. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-381\nComponents:\n - input (input) params={\"shape\":[1,1425]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1425]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1425,"outFeatures":303}},{"componentType":"linear","name":"enc2","params":{"inFeatures":303,"outFeatures":211}},{"componentType":"linear","name":"dec1","params":{"inFeatures":211,"outFeatures":303}},{"componentType":"linear","name":"dec2","params":{"inFeatures":303,"outFeatures":1425}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":991416}} {"id":"gen-cnn-382","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 50 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-382\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":26,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":7062}} {"id":"gen-txf-383","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 499-token sequences with 49 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-383\nComponents:\n - input (input) params={\"shape\":[1,499]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,499]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27737,"embeddingDim":26}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":26,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":725140}} {"id":"gen-gqa-384","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 273-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-384\nComponents:\n - input (input) params={\"shape\":[1,273]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,273]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46383,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":13379904}} {"id":"gen-trim-386","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3820-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-386\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - fc1 (linear) params={\"inFeatures\":235,\"outFeatures\":3820}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3820,\"outFeatures\":3820}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3820,\"outFeatures\":3820}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3820,\"outFeatures\":3820}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3820,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":235,"outFeatures":301}},{"type":"update_params","name":"fc2","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"fc3","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"fc4","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"head","params":{"inFeatures":301,"outFeatures":25}}],"grade":{"pass":true,"score":88,"params":350063}} {"id":"gen-norm-387","family":"norm","seed":20260716,"spec":"This 212-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-387\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - fc1 (linear) params={\"inFeatures\":212,\"outFeatures\":335}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":335,\"outFeatures\":40}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":40,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":335}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":40}}],"grade":{"pass":true,"score":94,"params":86100}} {"id":"gen-tower-388","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 143-feature user input and a 53-feature item input, each through its own 2-layer MLP tower ending at width 168, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-388\nComponents:\n - user_input (input) params={\"shape\":[1,143]}\n - item_input (input) params={\"shape\":[1,53]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,143]}},{"componentType":"input","name":"item_input","params":{"shape":[1,53]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":143,"outFeatures":168}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":168,"outFeatures":168}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":53,"outFeatures":168}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":168,"outFeatures":168}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":89712}} {"id":"gen-grow-389","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (61-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-389\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - fc1 (linear) params={\"inFeatures\":61,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":61,"outFeatures":834}},{"type":"update_params","name":"fc2","params":{"inFeatures":834,"outFeatures":834}},{"type":"update_params","name":"head","params":{"inFeatures":834,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":763944}} {"id":"gen-mlp-390","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 183-feature input with 181 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-390\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,183]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":183,"outFeatures":347}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":347,"outFeatures":907}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":907,"outFeatures":524}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":524,"outFeatures":942}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":942,"outFeatures":181}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1517608}} {"id":"gen-ae-391","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1151-dim input: encode down to a 237-dim bottleneck and decode back to 1151. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-391\nComponents:\n - input (input) params={\"shape\":[1,1151]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1151]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1151,"outFeatures":326}},{"componentType":"linear","name":"enc2","params":{"inFeatures":326,"outFeatures":237}},{"componentType":"linear","name":"dec1","params":{"inFeatures":237,"outFeatures":326}},{"componentType":"linear","name":"dec2","params":{"inFeatures":326,"outFeatures":1151}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":904976}} {"id":"gen-cnn-392","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 37 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-392\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":52,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":37295}} {"id":"gen-txf-393","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 408-token sequences with 14 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-393\nComponents:\n - input (input) params={\"shape\":[1,408]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,408]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36274,"embeddingDim":106}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":106,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":106,"outFeatures":14}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3891472}} {"id":"gen-gqa-394","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 342-token sequences with 99 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-394\nComponents:\n - input (input) params={\"shape\":[1,342]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,342]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33369,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":224,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7496832}} {"id":"gen-fix-395","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (484) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-395\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - embed (embedding) params={\"numEmbeddings\":45955,\"embeddingDim\":484}\n - attn (multiHeadAttention) params={\"embedDim\":484,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":484,\"outFeatures\":83}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":23219416}} {"id":"gen-trim-396","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2857-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-396\nComponents:\n - input (input) params={\"shape\":[1,222]}\n - fc1 (linear) params={\"inFeatures\":222,\"outFeatures\":2857}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2857,\"outFeatures\":2857}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2857,\"outFeatures\":2857}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2857,\"outFeatures\":2857}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2857,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":222,"outFeatures":297}},{"type":"update_params","name":"fc2","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"fc3","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"fc4","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"head","params":{"inFeatures":297,"outFeatures":28}}],"grade":{"pass":true,"score":88,"params":338877}} {"id":"gen-norm-397","family":"norm","seed":20260716,"spec":"This 199-feature, 75-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-397\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - fc1 (linear) params={\"inFeatures\":199,\"outFeatures\":378}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":378,\"outFeatures\":116}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":116,\"outFeatures\":346}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":346,\"outFeatures\":74}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":74,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":378}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":116}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":346}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":74}}],"grade":{"pass":true,"score":100,"params":190360}} {"id":"gen-tower-398","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 128-feature user input and a 87-feature item input, each through its own 2-layer MLP tower ending at width 347, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-398\nComponents:\n - user_input (input) params={\"shape\":[1,128]}\n - item_input (input) params={\"shape\":[1,87]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,128]}},{"componentType":"input","name":"item_input","params":{"shape":[1,87]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":128,"outFeatures":347}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":347,"outFeatures":347}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":87,"outFeatures":347}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":347,"outFeatures":347}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":694,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":316117}} {"id":"gen-grow-399","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (73-feature input, 31 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-399\nComponents:\n - input (input) params={\"shape\":[1,73]}\n - fc1 (linear) params={\"inFeatures\":73,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":73,"outFeatures":802}},{"type":"update_params","name":"fc2","params":{"inFeatures":802,"outFeatures":802}},{"type":"update_params","name":"head","params":{"inFeatures":802,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":726612}} {"id":"gen-mlp-400","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 103-feature input with 158 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-400\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,103]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":103,"outFeatures":68}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":68,"outFeatures":103}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":103,"outFeatures":224}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":224,"outFeatures":804}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":804,"outFeatures":253}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":253,"outFeatures":158}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":460562}} {"id":"gen-ae-401","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1642-dim input: encode down to a 255-dim bottleneck and decode back to 1642. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-401\nComponents:\n - input (input) params={\"shape\":[1,1642]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1642]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1642,"outFeatures":713}},{"componentType":"linear","name":"enc2","params":{"inFeatures":713,"outFeatures":255}},{"componentType":"linear","name":"dec1","params":{"inFeatures":255,"outFeatures":713}},{"componentType":"linear","name":"dec2","params":{"inFeatures":713,"outFeatures":1642}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2705122}} {"id":"gen-cnn-402","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 48 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-402\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":28,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":47,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":32,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":57,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":45288}} {"id":"gen-txf-403","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 323-token sequences with 51 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-403\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,323]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37585,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4000672}} {"id":"gen-gqa-404","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 220-token sequences with 83 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-404\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,220]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9403,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1517760}} {"id":"gen-trim-406","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2976-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-406\nComponents:\n - input (input) params={\"shape\":[1,247]}\n - fc1 (linear) params={\"inFeatures\":247,\"outFeatures\":2976}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2976,\"outFeatures\":2976}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2976,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":247,"outFeatures":230}},{"type":"update_params","name":"fc2","params":{"inFeatures":230,"outFeatures":230}},{"type":"update_params","name":"head","params":{"inFeatures":230,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":120060}} {"id":"gen-norm-407","family":"norm","seed":20260716,"spec":"This 146-feature, 10-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-407\nComponents:\n - input (input) params={\"shape\":[1,146]}\n - fc1 (linear) params={\"inFeatures\":146,\"outFeatures\":253}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":253,\"outFeatures\":69}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":69,\"outFeatures\":166}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":166,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":253}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":69}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":166}}],"grade":{"pass":true,"score":100,"params":67509}} {"id":"gen-tower-408","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 22-feature user input and a 89-feature item input, each through its own 2-layer MLP tower ending at width 417, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-408\nComponents:\n - user_input (input) params={\"shape\":[1,22]}\n - item_input (input) params={\"shape\":[1,89]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,22]}},{"componentType":"input","name":"item_input","params":{"shape":[1,89]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":22,"outFeatures":417}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":417,"outFeatures":417}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":89,"outFeatures":417}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":417,"outFeatures":417}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":834,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":394899}} {"id":"gen-grow-409","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (61-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-409\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - fc1 (linear) params={\"inFeatures\":61,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":61,"outFeatures":877}},{"type":"update_params","name":"fc2","params":{"inFeatures":877,"outFeatures":877}},{"type":"update_params","name":"head","params":{"inFeatures":877,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":857706}} {"id":"gen-mlp-410","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 31-feature input with 150 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-410\nComponents:\n - input (input) params={\"shape\":[1,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,31]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":31,"outFeatures":797}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":797,"outFeatures":309}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":309,"outFeatures":569}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":569,"outFeatures":150}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":532151}} {"id":"gen-ae-411","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1136-dim input: encode down to a 105-dim bottleneck and decode back to 1136. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-411\nComponents:\n - input (input) params={\"shape\":[1,1136]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1136]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1136,"outFeatures":444}},{"componentType":"linear","name":"enc2","params":{"inFeatures":444,"outFeatures":105}},{"componentType":"linear","name":"dec1","params":{"inFeatures":105,"outFeatures":444}},{"componentType":"linear","name":"dec2","params":{"inFeatures":444,"outFeatures":1136}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1102008}} {"id":"gen-cnn-412","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 31 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-412\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":32,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":19080}} {"id":"gen-txf-413","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 303-token sequences with 74 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-413\nComponents:\n - input (input) params={\"shape\":[1,303]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,303]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22288,"embeddingDim":24}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":24,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":24,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":24,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":543600}} {"id":"gen-gqa-414","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 461-token sequences with 73 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-414\nComponents:\n - input (input) params={\"shape\":[1,461]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,461]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42765,"embeddingDim":672}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":672,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[672]}},{"componentType":"linear","name":"head","params":{"inFeatures":672,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":28787136}} {"id":"gen-trim-416","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2053-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-416\nComponents:\n - input (input) params={\"shape\":[1,142]}\n - fc1 (linear) params={\"inFeatures\":142,\"outFeatures\":2053}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2053,\"outFeatures\":2053}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2053,\"outFeatures\":2053}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2053,\"outFeatures\":2053}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2053,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":142,"outFeatures":268}},{"type":"update_params","name":"fc2","params":{"inFeatures":268,"outFeatures":268}},{"type":"update_params","name":"fc3","params":{"inFeatures":268,"outFeatures":268}},{"type":"update_params","name":"fc4","params":{"inFeatures":268,"outFeatures":268}},{"type":"update_params","name":"head","params":{"inFeatures":268,"outFeatures":14}}],"grade":{"pass":true,"score":88,"params":257280}} {"id":"gen-norm-417","family":"norm","seed":20260716,"spec":"This 141-feature, 23-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-417\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - fc1 (linear) params={\"inFeatures\":141,\"outFeatures\":112}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":112,\"outFeatures\":127}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":127,\"outFeatures\":415}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":415,\"outFeatures\":433}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":433,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":112}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":127}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":415}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":433}}],"grade":{"pass":true,"score":100,"params":272375}} {"id":"gen-tower-418","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 115-feature user input and a 211-feature item input, each through its own 2-layer MLP tower ending at width 161, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-418\nComponents:\n - user_input (input) params={\"shape\":[1,115]}\n - item_input (input) params={\"shape\":[1,211]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,115]}},{"componentType":"input","name":"item_input","params":{"shape":[1,211]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":115,"outFeatures":161}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":161,"outFeatures":161}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":211,"outFeatures":161}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":161,"outFeatures":161}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":322,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":104650}} {"id":"gen-grow-419","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (19-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-419\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":19,"outFeatures":1123}},{"type":"update_params","name":"fc2","params":{"inFeatures":1123,"outFeatures":1123}},{"type":"update_params","name":"head","params":{"inFeatures":1123,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":1309418}} {"id":"gen-mlp-420","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 321-feature input with 31 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-420\nComponents:\n - input (input) params={\"shape\":[1,321]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,321]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":321,"outFeatures":561}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":561,"outFeatures":854}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":854,"outFeatures":461}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":461,"outFeatures":47}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":47,"outFeatures":890}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":890,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1143956}} {"id":"gen-ae-421","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1098-dim input: encode down to a 33-dim bottleneck and decode back to 1098. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-421\nComponents:\n - input (input) params={\"shape\":[1,1098]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1098]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1098,"outFeatures":637}},{"componentType":"linear","name":"enc2","params":{"inFeatures":637,"outFeatures":33}},{"componentType":"linear","name":"dec1","params":{"inFeatures":33,"outFeatures":637}},{"componentType":"linear","name":"dec2","params":{"inFeatures":637,"outFeatures":1098}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1440894}} {"id":"gen-cnn-422","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 75 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-422\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":42,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":5664}} {"id":"gen-txf-423","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 465-token sequences with 28 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-423\nComponents:\n - input (input) params={\"shape\":[1,465]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,465]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44266,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":176,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":176,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":8167456}} {"id":"gen-gqa-424","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 64-token sequences with 17 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-424\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,64]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34652,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":11648784}} {"id":"gen-trim-426","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2344-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-426\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - fc1 (linear) params={\"inFeatures\":237,\"outFeatures\":2344}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2344,\"outFeatures\":2344}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2344,\"outFeatures\":2344}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2344,\"outFeatures\":2344}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2344,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":237,"outFeatures":153}},{"type":"update_params","name":"fc2","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc3","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc4","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"head","params":{"inFeatures":153,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":109089}} {"id":"gen-norm-427","family":"norm","seed":20260716,"spec":"This 142-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-427\nComponents:\n - input (input) params={\"shape\":[1,142]}\n - fc1 (linear) params={\"inFeatures\":142,\"outFeatures\":177}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":177,\"outFeatures\":251}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":251,\"outFeatures\":102}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":102,\"outFeatures\":356}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":356,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":177}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":251}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":102}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":356}}],"grade":{"pass":true,"score":100,"params":150343}} {"id":"gen-tower-428","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 167-feature user input and a 84-feature item input, each through its own 2-layer MLP tower ending at width 336, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-428\nComponents:\n - user_input (input) params={\"shape\":[1,167]}\n - item_input (input) params={\"shape\":[1,84]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,167]}},{"componentType":"input","name":"item_input","params":{"shape":[1,84]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":167,"outFeatures":336}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":336,"outFeatures":336}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":84,"outFeatures":336}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":336,"outFeatures":336}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":672,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":310800}} {"id":"gen-grow-429","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (44-feature input, 31 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-429\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":699}},{"type":"update_params","name":"fc2","params":{"inFeatures":699,"outFeatures":699}},{"type":"update_params","name":"head","params":{"inFeatures":699,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":541026}} {"id":"gen-mlp-430","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 415-feature input with 55 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-430\nComponents:\n - input (input) params={\"shape\":[1,415]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,415]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":415,"outFeatures":811}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":811,"outFeatures":587}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":587,"outFeatures":243}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":243,"outFeatures":890}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":890,"outFeatures":94}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":94,"outFeatures":363}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":363,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1309280}} {"id":"gen-ae-431","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 566-dim input: encode down to a 28-dim bottleneck and decode back to 566. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-431\nComponents:\n - input (input) params={\"shape\":[1,566]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,566]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":566,"outFeatures":126}},{"componentType":"linear","name":"enc2","params":{"inFeatures":126,"outFeatures":28}},{"componentType":"linear","name":"dec1","params":{"inFeatures":28,"outFeatures":126}},{"componentType":"linear","name":"dec2","params":{"inFeatures":126,"outFeatures":566}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":149688}} {"id":"gen-cnn-432","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 84 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-432\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":27,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":51,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":84}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":20724}} {"id":"gen-txf-433","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 254-token sequences with 85 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-433\nComponents:\n - input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,254]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42584,"embeddingDim":156}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":156,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":156,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":156,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":156,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":6948396}} {"id":"gen-gqa-434","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 416-token sequences with 87 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-434\nComponents:\n - input (input) params={\"shape\":[1,416]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,416]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48000,"embeddingDim":100}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[100]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[100]}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4808700}} {"id":"gen-fix-435","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (148) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-435\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - embed (embedding) params={\"numEmbeddings\":46439,\"embeddingDim\":148}\n - attn (multiHeadAttention) params={\"embedDim\":148,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":148,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":6965472}} {"id":"gen-trim-436","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2483-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-436\nComponents:\n - input (input) params={\"shape\":[1,200]}\n - fc1 (linear) params={\"inFeatures\":200,\"outFeatures\":2483}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2483,\"outFeatures\":2483}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2483,\"outFeatures\":2483}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2483,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":200,"outFeatures":108}},{"type":"update_params","name":"fc2","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"fc3","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"head","params":{"inFeatures":108,"outFeatures":45}}],"grade":{"pass":true,"score":84,"params":49788}} {"id":"gen-norm-437","family":"norm","seed":20260716,"spec":"This 59-feature, 65-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-437\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":194}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":194,\"outFeatures\":187}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":187,\"outFeatures\":65}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":194}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":187}}],"grade":{"pass":true,"score":94,"params":59879}} {"id":"gen-tower-438","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 175-feature user input and a 170-feature item input, each through its own 2-layer MLP tower ending at width 425, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-438\nComponents:\n - user_input (input) params={\"shape\":[1,175]}\n - item_input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,175]}},{"componentType":"input","name":"item_input","params":{"shape":[1,170]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":175,"outFeatures":425}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":170,"outFeatures":425}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":850,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":508725}} {"id":"gen-grow-439","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-439\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":898}},{"type":"update_params","name":"fc2","params":{"inFeatures":898,"outFeatures":898}},{"type":"update_params","name":"head","params":{"inFeatures":898,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":875550}} {"id":"gen-mlp-440","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 206-feature input with 111 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-440\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,206]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":206,"outFeatures":231}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":231,"outFeatures":542}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":542,"outFeatures":111}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":232950}} {"id":"gen-ae-441","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 534-dim input: encode down to a 244-dim bottleneck and decode back to 534. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-441\nComponents:\n - input (input) params={\"shape\":[1,534]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,534]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":534,"outFeatures":833}},{"componentType":"linear","name":"enc2","params":{"inFeatures":833,"outFeatures":244}},{"componentType":"linear","name":"dec1","params":{"inFeatures":244,"outFeatures":833}},{"componentType":"linear","name":"dec2","params":{"inFeatures":833,"outFeatures":534}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1296148}} {"id":"gen-cnn-442","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 34 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-442\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":39,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":45038}} {"id":"gen-txf-443","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 161-token sequences with 64 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-443\nComponents:\n - input (input) params={\"shape\":[1,161]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,161]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36438,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":88,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3305104}} {"id":"gen-gqa-444","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 483-token sequences with 26 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-444\nComponents:\n - input (input) params={\"shape\":[1,483]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,483]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42079,"embeddingDim":100}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[100]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[100]}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4210500}} {"id":"gen-fix-445","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (114) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-445\nComponents:\n - input (input) params={\"shape\":[1,385]}\n - embed (embedding) params={\"numEmbeddings\":11447,\"embeddingDim\":114}\n - attn (multiHeadAttention) params={\"embedDim\":114,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":114,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":1359222}} {"id":"gen-trim-446","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1747-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-446\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - fc1 (linear) params={\"inFeatures\":86,\"outFeatures\":1747}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1747,\"outFeatures\":1747}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1747,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":86,"outFeatures":103}},{"type":"update_params","name":"fc2","params":{"inFeatures":103,"outFeatures":103}},{"type":"update_params","name":"head","params":{"inFeatures":103,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":20497}} {"id":"gen-norm-447","family":"norm","seed":20260716,"spec":"This 144-feature, 13-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-447\nComponents:\n - input (input) params={\"shape\":[1,144]}\n - fc1 (linear) params={\"inFeatures\":144,\"outFeatures\":500}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":500,\"outFeatures\":154}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":154,\"outFeatures\":339}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":339,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":500}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":154}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":339}}],"grade":{"pass":true,"score":100,"params":205613}} {"id":"gen-tower-448","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 170-feature user input and a 35-feature item input, each through its own 2-layer MLP tower ending at width 364, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-448\nComponents:\n - user_input (input) params={\"shape\":[1,170]}\n - item_input (input) params={\"shape\":[1,35]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,170]}},{"componentType":"input","name":"item_input","params":{"shape":[1,35]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":170,"outFeatures":364}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":364,"outFeatures":364}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":35,"outFeatures":364}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":364,"outFeatures":364}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":728,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":340340}} {"id":"gen-grow-449","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (108-feature input, 47 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-449\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":734}},{"type":"update_params","name":"fc2","params":{"inFeatures":734,"outFeatures":734}},{"type":"update_params","name":"head","params":{"inFeatures":734,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":652526}} {"id":"gen-mlp-450","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 228-feature input with 10 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-450\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":228,"outFeatures":666}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":666,"outFeatures":108}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":108,"outFeatures":844}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":844,"outFeatures":244}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":244,"outFeatures":366}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":366,"outFeatures":578}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":578,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":827496}} {"id":"gen-ae-451","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2016-dim input: encode down to a 229-dim bottleneck and decode back to 2016. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-451\nComponents:\n - input (input) params={\"shape\":[1,2016]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2016]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2016,"outFeatures":389}},{"componentType":"linear","name":"enc2","params":{"inFeatures":389,"outFeatures":229}},{"componentType":"linear","name":"dec1","params":{"inFeatures":229,"outFeatures":389}},{"componentType":"linear","name":"dec2","params":{"inFeatures":389,"outFeatures":2016}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1746610}} {"id":"gen-cnn-452","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 62 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-452\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":48,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":32912}} {"id":"gen-txf-453","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 318-token sequences with 91 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-453\nComponents:\n - input (input) params={\"shape\":[1,318]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,318]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38676,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":7913400}} {"id":"gen-gqa-454","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 102-token sequences with 37 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-454\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,102]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31902,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5110240}} {"id":"gen-fix-455","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (236) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-455\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - embed (embedding) params={\"numEmbeddings\":26615,\"embeddingDim\":236}\n - attn (multiHeadAttention) params={\"embedDim\":236,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":236,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":6511240}} {"id":"gen-trim-456","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3243-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-456\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - fc1 (linear) params={\"inFeatures\":39,\"outFeatures\":3243}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3243,\"outFeatures\":3243}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3243,\"outFeatures\":3243}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3243,\"outFeatures\":3243}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3243,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":39,"outFeatures":153}},{"type":"update_params","name":"fc2","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc3","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc4","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"head","params":{"inFeatures":153,"outFeatures":24}}],"grade":{"pass":true,"score":88,"params":79866}} {"id":"gen-norm-457","family":"norm","seed":20260716,"spec":"This 24-feature, 65-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-457\nComponents:\n - input (input) params={\"shape\":[1,24]}\n - fc1 (linear) params={\"inFeatures\":24,\"outFeatures\":346}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":346,\"outFeatures\":469}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":469,\"outFeatures\":457}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":457,\"outFeatures\":284}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":284,\"outFeatures\":394}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":394,\"outFeatures\":65}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":346}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":469}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":457}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":284}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":394}}],"grade":{"pass":true,"score":100,"params":652205}} {"id":"gen-tower-458","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 64-feature user input and a 256-feature item input, each through its own 2-layer MLP tower ending at width 407, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-458\nComponents:\n - user_input (input) params={\"shape\":[1,64]}\n - item_input (input) params={\"shape\":[1,256]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,64]}},{"componentType":"input","name":"item_input","params":{"shape":[1,256]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":64,"outFeatures":407}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":407,"outFeatures":407}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":256,"outFeatures":407}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":407,"outFeatures":407}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":814,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":462352}} {"id":"gen-grow-459","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (72-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-459\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":72,"outFeatures":1149}},{"type":"update_params","name":"fc2","params":{"inFeatures":1149,"outFeatures":1149}},{"type":"update_params","name":"head","params":{"inFeatures":1149,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1427058}} {"id":"gen-mlp-460","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 248-feature input with 20 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-460\nComponents:\n - input (input) params={\"shape\":[1,248]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,248]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":248,"outFeatures":247}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":247,"outFeatures":704}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":704,"outFeatures":622}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":622,"outFeatures":41}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":41,"outFeatures":159}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":159,"outFeatures":425}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":425,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":781128}} {"id":"gen-ae-461","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2012-dim input: encode down to a 46-dim bottleneck and decode back to 2012. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-461\nComponents:\n - input (input) params={\"shape\":[1,2012]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2012]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2012,"outFeatures":331}},{"componentType":"linear","name":"enc2","params":{"inFeatures":331,"outFeatures":46}},{"componentType":"linear","name":"dec1","params":{"inFeatures":46,"outFeatures":331}},{"componentType":"linear","name":"dec2","params":{"inFeatures":331,"outFeatures":2012}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1362396}} {"id":"gen-cnn-462","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 15 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-462\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":38,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":34,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":40848}} {"id":"gen-txf-463","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 393-token sequences with 83 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-463\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33431,"embeddingDim":472}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":472,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":472,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":472,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":17600880}} {"id":"gen-gqa-464","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 235-token sequences with 57 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-464\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,235]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35301,"embeddingDim":208}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":208,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[208]}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":7354464}} {"id":"gen-fix-465","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (254) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-465\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - embed (embedding) params={\"numEmbeddings\":37355,\"embeddingDim\":254}\n - attn (multiHeadAttention) params={\"embedDim\":254,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":254,\"outFeatures\":79}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":9766300}} {"id":"gen-trim-466","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1979-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-466\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":1979}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1979,\"outFeatures\":1979}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1979,\"outFeatures\":1979}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1979,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":90,"outFeatures":368}},{"type":"update_params","name":"fc2","params":{"inFeatures":368,"outFeatures":368}},{"type":"update_params","name":"fc3","params":{"inFeatures":368,"outFeatures":368}},{"type":"update_params","name":"head","params":{"inFeatures":368,"outFeatures":45}}],"grade":{"pass":true,"score":84,"params":320528}} {"id":"gen-norm-467","family":"norm","seed":20260716,"spec":"This 106-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-467\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":233}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":233,\"outFeatures\":287}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":287,\"outFeatures\":46}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":46,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":233}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":287}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":46}}],"grade":{"pass":true,"score":100,"params":106289}} {"id":"gen-tower-468","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 141-feature user input and a 102-feature item input, each through its own 2-layer MLP tower ending at width 475, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-468\nComponents:\n - user_input (input) params={\"shape\":[1,141]}\n - item_input (input) params={\"shape\":[1,102]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,141]}},{"componentType":"input","name":"item_input","params":{"shape":[1,102]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":141,"outFeatures":475}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":475,"outFeatures":475}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":102,"outFeatures":475}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":475,"outFeatures":475}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":950,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":567625}} {"id":"gen-grow-469","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (82-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-469\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":82,"outFeatures":1118}},{"type":"update_params","name":"fc2","params":{"inFeatures":1118,"outFeatures":1118}},{"type":"update_params","name":"head","params":{"inFeatures":1118,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1365078}} {"id":"gen-mlp-470","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 163-feature input with 75 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-470\nComponents:\n - input (input) params={\"shape\":[1,163]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,163]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":163,"outFeatures":878}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":878,"outFeatures":317}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":317,"outFeatures":655}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":655,"outFeatures":440}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":440,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":950275}} {"id":"gen-ae-471","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 588-dim input: encode down to a 19-dim bottleneck and decode back to 588. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-471\nComponents:\n - input (input) params={\"shape\":[1,588]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,588]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":588,"outFeatures":416}},{"componentType":"linear","name":"enc2","params":{"inFeatures":416,"outFeatures":19}},{"componentType":"linear","name":"dec1","params":{"inFeatures":19,"outFeatures":416}},{"componentType":"linear","name":"dec2","params":{"inFeatures":416,"outFeatures":588}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":505024}} {"id":"gen-cnn-472","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 22 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-472\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":61,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":37930}} {"id":"gen-txf-473","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 235-token sequences with 91 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-473\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,235]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30233,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":80,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":80,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2502720}} {"id":"gen-gqa-474","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 219-token sequences with 78 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-474\nComponents:\n - input (input) params={\"shape\":[1,219]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,219]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15591,"embeddingDim":416}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":416,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[416]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":416,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[416]}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6518304}} {"id":"gen-fix-475","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (458) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-475\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - embed (embedding) params={\"numEmbeddings\":25207,\"embeddingDim\":458}\n - attn (multiHeadAttention) params={\"embedDim\":458,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":458,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":12406762}} {"id":"gen-trim-476","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1599-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-476\nComponents:\n - input (input) params={\"shape\":[1,244]}\n - fc1 (linear) params={\"inFeatures\":244,\"outFeatures\":1599}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1599,\"outFeatures\":1599}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1599,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":244,"outFeatures":179}},{"type":"update_params","name":"fc2","params":{"inFeatures":179,"outFeatures":179}},{"type":"update_params","name":"head","params":{"inFeatures":179,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":81982}} {"id":"gen-norm-477","family":"norm","seed":20260716,"spec":"This 110-feature, 22-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-477\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":455}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":455,\"outFeatures\":327}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":327,\"outFeatures\":167}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":167,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":455}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":327}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":167}}],"grade":{"pass":true,"score":100,"params":257118}} {"id":"gen-tower-478","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 148-feature user input and a 153-feature item input, each through its own 2-layer MLP tower ending at width 94, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-478\nComponents:\n - user_input (input) params={\"shape\":[1,148]}\n - item_input (input) params={\"shape\":[1,153]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,148]}},{"componentType":"input","name":"item_input","params":{"shape":[1,153]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":148,"outFeatures":94}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":153,"outFeatures":94}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":188,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46154}} {"id":"gen-grow-479","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (49-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-479\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":1309}},{"type":"update_params","name":"fc2","params":{"inFeatures":1309,"outFeatures":1309}},{"type":"update_params","name":"head","params":{"inFeatures":1309,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":1803802}} {"id":"gen-mlp-480","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 43-feature input with 101 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-480\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,43]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":43,"outFeatures":787}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":787,"outFeatures":760}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":760,"outFeatures":466}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":466,"outFeatures":101}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1033187}} {"id":"gen-ae-481","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 342-dim input: encode down to a 200-dim bottleneck and decode back to 342. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-481\nComponents:\n - input (input) params={\"shape\":[1,342]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,342]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":342,"outFeatures":1011}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1011,"outFeatures":200}},{"componentType":"linear","name":"dec1","params":{"inFeatures":200,"outFeatures":1011}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1011,"outFeatures":342}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1095924}} {"id":"gen-cnn-482","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 29 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-482\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":5313}} {"id":"gen-txf-483","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 202-token sequences with 87 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-483\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,202]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20604,"embeddingDim":58}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":58,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":58,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1226990}} {"id":"gen-gqa-484","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 324-token sequences with 63 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-484\nComponents:\n - input (input) params={\"shape\":[1,324]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,324]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15422,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3964160}} {"id":"gen-trim-486","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2136-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-486\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":2136}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2136,\"outFeatures\":2136}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2136,\"outFeatures\":2136}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2136,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":112}},{"type":"update_params","name":"fc2","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"fc3","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"head","params":{"inFeatures":112,"outFeatures":27}}],"grade":{"pass":true,"score":84,"params":40208}} {"id":"gen-norm-487","family":"norm","seed":20260716,"spec":"This 21-feature, 90-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-487\nComponents:\n - input (input) params={\"shape\":[1,21]}\n - fc1 (linear) params={\"inFeatures\":21,\"outFeatures\":241}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":241,\"outFeatures\":188}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":188,\"outFeatures\":324}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":324,\"outFeatures\":90}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":241}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":188}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":324}}],"grade":{"pass":true,"score":100,"params":140441}} {"id":"gen-tower-488","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 30-feature user input and a 28-feature item input, each through its own 2-layer MLP tower ending at width 446, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-488\nComponents:\n - user_input (input) params={\"shape\":[1,30]}\n - item_input (input) params={\"shape\":[1,28]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,30]}},{"componentType":"input","name":"item_input","params":{"shape":[1,28]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":30,"outFeatures":446}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":446,"outFeatures":446}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":28,"outFeatures":446}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":446,"outFeatures":446}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":892,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":424592}} {"id":"gen-grow-489","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-489\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":904}},{"type":"update_params","name":"fc2","params":{"inFeatures":904,"outFeatures":904}},{"type":"update_params","name":"head","params":{"inFeatures":904,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":890440}} {"id":"gen-mlp-490","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 21-feature input with 4 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-490\nComponents:\n - input (input) params={\"shape\":[1,21]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,21]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":21,"outFeatures":1017}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":1017,"outFeatures":147}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":147,"outFeatures":84}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":84,"outFeatures":116}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":116,"outFeatures":239}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":239,"outFeatures":690}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":690,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":388342}} {"id":"gen-ae-491","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2034-dim input: encode down to a 183-dim bottleneck and decode back to 2034. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-491\nComponents:\n - input (input) params={\"shape\":[1,2034]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2034]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2034,"outFeatures":285}},{"componentType":"linear","name":"enc2","params":{"inFeatures":285,"outFeatures":183}},{"componentType":"linear","name":"dec1","params":{"inFeatures":183,"outFeatures":285}},{"componentType":"linear","name":"dec2","params":{"inFeatures":285,"outFeatures":2034}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1263690}} {"id":"gen-cnn-492","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x26x26 image with 66 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-492\nComponents:\n - input (input) params={\"shape\":[1,3,26,26]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,26,26]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":49,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":63,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":60,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":15,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":72216}} {"id":"gen-txf-493","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 409-token sequences with 3 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-493\nComponents:\n - input (input) params={\"shape\":[1,409]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,409]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41397,"embeddingDim":40}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":40,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1675200}} {"id":"gen-gqa-494","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 71-token sequences with 54 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-494\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,71]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25579,"embeddingDim":768}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":768,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[768]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":768,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[768]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":768,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[768]}},{"componentType":"linear","name":"head","params":{"inFeatures":768,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":19686144}} {"id":"gen-fix-495","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (218) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-495\nComponents:\n - input (input) params={\"shape\":[1,383]}\n - embed (embedding) params={\"numEmbeddings\":20823,\"embeddingDim\":218}\n - attn (multiHeadAttention) params={\"embedDim\":218,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":218,\"outFeatures\":58}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4742154}} {"id":"gen-trim-496","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1742-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-496\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":1742}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1742,\"outFeatures\":1742}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1742,\"outFeatures\":1742}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1742,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":190,"outFeatures":94}},{"type":"update_params","name":"fc2","params":{"inFeatures":94,"outFeatures":94}},{"type":"update_params","name":"fc3","params":{"inFeatures":94,"outFeatures":94}},{"type":"update_params","name":"head","params":{"inFeatures":94,"outFeatures":16}}],"grade":{"pass":true,"score":84,"params":37036}} {"id":"gen-norm-497","family":"norm","seed":20260716,"spec":"This 223-feature, 8-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-497\nComponents:\n - input (input) params={\"shape\":[1,223]}\n - fc1 (linear) params={\"inFeatures\":223,\"outFeatures\":238}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":238,\"outFeatures\":270}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":270,\"outFeatures\":351}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":351,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":238}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":270}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":351}}],"grade":{"pass":true,"score":100,"params":214912}} {"id":"gen-tower-498","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 82-feature user input and a 243-feature item input, each through its own 2-layer MLP tower ending at width 479, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-498\nComponents:\n - user_input (input) params={\"shape\":[1,82]}\n - item_input (input) params={\"shape\":[1,243]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,82]}},{"componentType":"input","name":"item_input","params":{"shape":[1,243]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":82,"outFeatures":479}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":479,"outFeatures":479}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":243,"outFeatures":479}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":479,"outFeatures":479}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":958,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":615515}} {"id":"gen-grow-499","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (44-feature input, 17 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-499\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":855}},{"type":"update_params","name":"fc2","params":{"inFeatures":855,"outFeatures":855}},{"type":"update_params","name":"head","params":{"inFeatures":855,"outFeatures":17}}],"grade":{"pass":true,"score":80,"params":783180}} {"id":"gen-mlp-500","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 442-feature input with 116 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-500\nComponents:\n - input (input) params={\"shape\":[1,442]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,442]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":442,"outFeatures":128}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":128,"outFeatures":775}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":775,"outFeatures":431}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":431,"outFeatures":843}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":843,"outFeatures":189}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":189,"outFeatures":893}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":893,"outFeatures":116}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1284826}} {"id":"gen-ae-501","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1229-dim input: encode down to a 160-dim bottleneck and decode back to 1229. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-501\nComponents:\n - input (input) params={\"shape\":[1,1229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1229]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1229,"outFeatures":112}},{"componentType":"linear","name":"enc2","params":{"inFeatures":112,"outFeatures":160}},{"componentType":"linear","name":"dec1","params":{"inFeatures":160,"outFeatures":112}},{"componentType":"linear","name":"dec2","params":{"inFeatures":112,"outFeatures":1229}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":311136}} {"id":"gen-cnn-502","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 98 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-502\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":19484}} {"id":"gen-txf-503","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 332-token sequences with 8 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-503\nComponents:\n - input (input) params={\"shape\":[1,332]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,332]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9750,"embeddingDim":232}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":232,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2479152}} {"id":"gen-gqa-504","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 407-token sequences with 17 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-504\nComponents:\n - input (input) params={\"shape\":[1,407]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,407]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42375,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":6782720}} {"id":"gen-fix-505","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (448) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-505\nComponents:\n - input (input) params={\"shape\":[1,316]}\n - embed (embedding) params={\"numEmbeddings\":47077,\"embeddingDim\":448}\n - attn (multiHeadAttention) params={\"embedDim\":448,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":448,\"outFeatures\":68}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":21923776}} {"id":"gen-trim-506","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2378-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-506\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - fc1 (linear) params={\"inFeatures\":123,\"outFeatures\":2378}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2378,\"outFeatures\":2378}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2378,\"outFeatures\":2378}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2378,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":123,"outFeatures":187}},{"type":"update_params","name":"fc2","params":{"inFeatures":187,"outFeatures":187}},{"type":"update_params","name":"fc3","params":{"inFeatures":187,"outFeatures":187}},{"type":"update_params","name":"head","params":{"inFeatures":187,"outFeatures":6}}],"grade":{"pass":true,"score":84,"params":94061}} {"id":"gen-norm-507","family":"norm","seed":20260716,"spec":"This 79-feature, 70-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-507\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - fc1 (linear) params={\"inFeatures\":79,\"outFeatures\":466}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":466,\"outFeatures\":222}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":222,\"outFeatures\":187}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":187,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":466}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":222}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":187}}],"grade":{"pass":true,"score":100,"params":194870}} {"id":"gen-tower-508","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 51-feature user input and a 254-feature item input, each through its own 2-layer MLP tower ending at width 261, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-508\nComponents:\n - user_input (input) params={\"shape\":[1,51]}\n - item_input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,51]}},{"componentType":"input","name":"item_input","params":{"shape":[1,254]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":51,"outFeatures":261}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":261,"outFeatures":261}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":254,"outFeatures":261}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":261,"outFeatures":261}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":522,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":216369}} {"id":"gen-grow-509","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (126-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-509\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":126,"outFeatures":742}},{"type":"update_params","name":"fc2","params":{"inFeatures":742,"outFeatures":742}},{"type":"update_params","name":"head","params":{"inFeatures":742,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":655928}} {"id":"gen-mlp-510","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 389-feature input with 13 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-510\nComponents:\n - input (input) params={\"shape\":[1,389]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,389]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":389,"outFeatures":671}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":671,"outFeatures":632}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":632,"outFeatures":389}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":389,"outFeatures":98}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":98,"outFeatures":575}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":575,"outFeatures":505}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":505,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1322351}} {"id":"gen-ae-511","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1357-dim input: encode down to a 20-dim bottleneck and decode back to 1357. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-511\nComponents:\n - input (input) params={\"shape\":[1,1357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1357]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1357,"outFeatures":444}},{"componentType":"linear","name":"enc2","params":{"inFeatures":444,"outFeatures":20}},{"componentType":"linear","name":"dec1","params":{"inFeatures":20,"outFeatures":444}},{"componentType":"linear","name":"dec2","params":{"inFeatures":444,"outFeatures":1357}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1222776}} {"id":"gen-cnn-512","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 92 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-512\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":20588}} {"id":"gen-txf-513","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 402-token sequences with 13 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-513\nComponents:\n - input (input) params={\"shape\":[1,402]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,402]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18781,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2041104}} {"id":"gen-gqa-514","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 229-token sequences with 10 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-514\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,229]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41228,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":136,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5608368}} {"id":"gen-trim-516","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3024-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-516\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":3024}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3024,\"outFeatures\":3024}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3024,\"outFeatures\":3024}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3024,\"outFeatures\":3024}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3024,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":98,"outFeatures":114}},{"type":"update_params","name":"fc2","params":{"inFeatures":114,"outFeatures":114}},{"type":"update_params","name":"fc3","params":{"inFeatures":114,"outFeatures":114}},{"type":"update_params","name":"fc4","params":{"inFeatures":114,"outFeatures":114}},{"type":"update_params","name":"head","params":{"inFeatures":114,"outFeatures":9}}],"grade":{"pass":true,"score":88,"params":51186}} {"id":"gen-norm-517","family":"norm","seed":20260716,"spec":"This 76-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-517\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":198}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":198,\"outFeatures\":452}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":452,\"outFeatures\":223}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":223,\"outFeatures\":430}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":430,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":198}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":452}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":223}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":430}}],"grade":{"pass":true,"score":100,"params":319290}} {"id":"gen-tower-518","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 78-feature user input and a 91-feature item input, each through its own 2-layer MLP tower ending at width 132, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-518\nComponents:\n - user_input (input) params={\"shape\":[1,78]}\n - item_input (input) params={\"shape\":[1,91]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,78]}},{"componentType":"input","name":"item_input","params":{"shape":[1,91]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":78,"outFeatures":132}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":132,"outFeatures":132}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":91,"outFeatures":132}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":132,"outFeatures":132}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":57420}} {"id":"gen-grow-519","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (116-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-519\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":1323}},{"type":"update_params","name":"fc2","params":{"inFeatures":1323,"outFeatures":1323}},{"type":"update_params","name":"head","params":{"inFeatures":1323,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":1956717}} {"id":"gen-mlp-520","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 383-feature input with 138 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-520\nComponents:\n - input (input) params={\"shape\":[1,383]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,383]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":383,"outFeatures":553}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":553,"outFeatures":69}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":69,"outFeatures":138}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":259478}} {"id":"gen-ae-521","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1389-dim input: encode down to a 62-dim bottleneck and decode back to 1389. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-521\nComponents:\n - input (input) params={\"shape\":[1,1389]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1389]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1389,"outFeatures":886}},{"componentType":"linear","name":"enc2","params":{"inFeatures":886,"outFeatures":62}},{"componentType":"linear","name":"dec1","params":{"inFeatures":62,"outFeatures":886}},{"componentType":"linear","name":"dec2","params":{"inFeatures":886,"outFeatures":1389}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2571172}} {"id":"gen-cnn-522","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 32 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-522\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":17,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":24,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":12744}} {"id":"gen-txf-523","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 165-token sequences with 78 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-523\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,165]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36285,"embeddingDim":90}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":90,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":90,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3305070}} {"id":"gen-gqa-524","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 108-token sequences with 34 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-524\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,108]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23545,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6036224}} {"id":"gen-fix-525","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (464) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-525\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - embed (embedding) params={\"numEmbeddings\":13174,\"embeddingDim\":464}\n - attn (multiHeadAttention) params={\"embedDim\":464,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":464,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":6995728}} {"id":"gen-trim-526","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2199-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-526\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - fc1 (linear) params={\"inFeatures\":236,\"outFeatures\":2199}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2199,\"outFeatures\":2199}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2199,\"outFeatures\":2199}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2199,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":236,"outFeatures":99}},{"type":"update_params","name":"fc2","params":{"inFeatures":99,"outFeatures":99}},{"type":"update_params","name":"fc3","params":{"inFeatures":99,"outFeatures":99}},{"type":"update_params","name":"head","params":{"inFeatures":99,"outFeatures":37}}],"grade":{"pass":true,"score":84,"params":46629}} {"id":"gen-norm-527","family":"norm","seed":20260716,"spec":"This 93-feature, 40-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-527\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - fc1 (linear) params={\"inFeatures\":93,\"outFeatures\":42}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":42,\"outFeatures\":276}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":276,\"outFeatures\":437}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":437,\"outFeatures\":478}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":478,\"outFeatures\":477}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":477,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":42}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":437}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":478}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":477}}],"grade":{"pass":true,"score":100,"params":592082}} {"id":"gen-tower-528","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 69-feature user input and a 106-feature item input, each through its own 2-layer MLP tower ending at width 114, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-528\nComponents:\n - user_input (input) params={\"shape\":[1,69]}\n - item_input (input) params={\"shape\":[1,106]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,69]}},{"componentType":"input","name":"item_input","params":{"shape":[1,106]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":69,"outFeatures":114}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":114,"outFeatures":114}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":106,"outFeatures":114}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":114,"outFeatures":114}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":228,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46170}} {"id":"gen-grow-529","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (95-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-529\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":95,"outFeatures":1344}},{"type":"update_params","name":"fc2","params":{"inFeatures":1344,"outFeatures":1344}},{"type":"update_params","name":"head","params":{"inFeatures":1344,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":1940736}} {"id":"gen-mlp-530","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 305-feature input with 80 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-530\nComponents:\n - input (input) params={\"shape\":[1,305]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,305]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":305,"outFeatures":448}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":448,"outFeatures":721}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":721,"outFeatures":989}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":989,"outFeatures":453}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":453,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1656974}} {"id":"gen-ae-531","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2047-dim input: encode down to a 214-dim bottleneck and decode back to 2047. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-531\nComponents:\n - input (input) params={\"shape\":[1,2047]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2047]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2047,"outFeatures":506}},{"componentType":"linear","name":"enc2","params":{"inFeatures":506,"outFeatures":214}},{"componentType":"linear","name":"dec1","params":{"inFeatures":214,"outFeatures":506}},{"componentType":"linear","name":"dec2","params":{"inFeatures":506,"outFeatures":2047}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2288132}} {"id":"gen-cnn-532","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 45 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-532\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":45189}} {"id":"gen-txf-533","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 428-token sequences with 73 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-533\nComponents:\n - input (input) params={\"shape\":[1,428]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,428]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14594,"embeddingDim":18}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":18,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":18,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":266598}} {"id":"gen-gqa-534","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 109-token sequences with 86 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-534\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,109]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44277,"embeddingDim":656}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[656]}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":29102128}} {"id":"gen-trim-536","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3659-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-536\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":3659}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3659,\"outFeatures\":3659}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3659,\"outFeatures\":3659}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3659,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":250}},{"type":"update_params","name":"fc2","params":{"inFeatures":250,"outFeatures":250}},{"type":"update_params","name":"fc3","params":{"inFeatures":250,"outFeatures":250}},{"type":"update_params","name":"head","params":{"inFeatures":250,"outFeatures":46}}],"grade":{"pass":true,"score":84,"params":152000}} {"id":"gen-norm-537","family":"norm","seed":20260716,"spec":"This 42-feature, 95-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-537\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":110}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":110,\"outFeatures\":277}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":277,\"outFeatures\":477}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":477,\"outFeatures\":420}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":420,\"outFeatures\":193}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":193,\"outFeatures\":95}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":110}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":277}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":477}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":420}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":193}}],"grade":{"pass":true,"score":100,"params":466954}} {"id":"gen-tower-538","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 30-feature user input and a 39-feature item input, each through its own 2-layer MLP tower ending at width 108, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-538\nComponents:\n - user_input (input) params={\"shape\":[1,30]}\n - item_input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,30]}},{"componentType":"input","name":"item_input","params":{"shape":[1,39]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":30,"outFeatures":108}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":108,"outFeatures":108}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":39,"outFeatures":108}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":108,"outFeatures":108}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30996}} {"id":"gen-grow-539","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (40-feature input, 30 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-539\nComponents:\n - input (input) params={\"shape\":[1,40]}\n - fc1 (linear) params={\"inFeatures\":40,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":40,"outFeatures":696}},{"type":"update_params","name":"fc2","params":{"inFeatures":696,"outFeatures":696}},{"type":"update_params","name":"head","params":{"inFeatures":696,"outFeatures":30}}],"grade":{"pass":true,"score":80,"params":533136}} {"id":"gen-mlp-540","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 357-feature input with 43 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-540\nComponents:\n - input (input) params={\"shape\":[1,357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,357]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":357,"outFeatures":607}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":607,"outFeatures":1006}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1006,"outFeatures":137}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":137,"outFeatures":908}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":908,"outFeatures":104}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":104,"outFeatures":398}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":398,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1242497}} {"id":"gen-ae-541","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 108-dim input: encode down to a 115-dim bottleneck and decode back to 108. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-541\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,108]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":108,"outFeatures":684}},{"componentType":"linear","name":"enc2","params":{"inFeatures":684,"outFeatures":115}},{"componentType":"linear","name":"dec1","params":{"inFeatures":115,"outFeatures":684}},{"componentType":"linear","name":"dec2","params":{"inFeatures":684,"outFeatures":108}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":305064}} {"id":"gen-cnn-542","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 59 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-542\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":9,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":10680}} {"id":"gen-txf-543","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 149-token sequences with 60 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-543\nComponents:\n - input (input) params={\"shape\":[1,149]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,149]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33799,"embeddingDim":208}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":208,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":208,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7388784}} {"id":"gen-gqa-544","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 158-token sequences with 92 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-544\nComponents:\n - input (input) params={\"shape\":[1,158]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,158]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10388,"embeddingDim":156}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":156,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[156]}},{"componentType":"linear","name":"head","params":{"inFeatures":156,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1634880}} {"id":"gen-fix-545","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (206) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-545\nComponents:\n - input (input) params={\"shape\":[1,402]}\n - embed (embedding) params={\"numEmbeddings\":29382,\"embeddingDim\":206}\n - attn (multiHeadAttention) params={\"embedDim\":206,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":206,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":6235002}} {"id":"gen-trim-546","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2060-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-546\nComponents:\n - input (input) params={\"shape\":[1,174]}\n - fc1 (linear) params={\"inFeatures\":174,\"outFeatures\":2060}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2060,\"outFeatures\":2060}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2060,\"outFeatures\":2060}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2060,\"outFeatures\":2060}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2060,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":174,"outFeatures":232}},{"type":"update_params","name":"fc2","params":{"inFeatures":232,"outFeatures":232}},{"type":"update_params","name":"fc3","params":{"inFeatures":232,"outFeatures":232}},{"type":"update_params","name":"fc4","params":{"inFeatures":232,"outFeatures":232}},{"type":"update_params","name":"head","params":{"inFeatures":232,"outFeatures":26}}],"grade":{"pass":true,"score":88,"params":207872}} {"id":"gen-norm-547","family":"norm","seed":20260716,"spec":"This 89-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-547\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - fc1 (linear) params={\"inFeatures\":89,\"outFeatures\":139}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":139,\"outFeatures\":334}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":334,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":139}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":334}}],"grade":{"pass":true,"score":94,"params":73827}} {"id":"gen-tower-548","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 74-feature user input and a 107-feature item input, each through its own 2-layer MLP tower ending at width 147, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-548\nComponents:\n - user_input (input) params={\"shape\":[1,74]}\n - item_input (input) params={\"shape\":[1,107]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,74]}},{"componentType":"input","name":"item_input","params":{"shape":[1,107]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":74,"outFeatures":147}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":147,"outFeatures":147}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":107,"outFeatures":147}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":147,"outFeatures":147}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":294,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":70119}} {"id":"gen-grow-549","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (58-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-549\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - fc1 (linear) params={\"inFeatures\":58,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":58,"outFeatures":947}},{"type":"update_params","name":"fc2","params":{"inFeatures":947,"outFeatures":947}},{"type":"update_params","name":"head","params":{"inFeatures":947,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":974463}} {"id":"gen-mlp-550","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 389-feature input with 200 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-550\nComponents:\n - input (input) params={\"shape\":[1,389]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,389]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":389,"outFeatures":789}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":789,"outFeatures":894}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":894,"outFeatures":537}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":537,"outFeatures":157}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":157,"outFeatures":200}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1608074}} {"id":"gen-ae-551","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 574-dim input: encode down to a 186-dim bottleneck and decode back to 574. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-551\nComponents:\n - input (input) params={\"shape\":[1,574]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,574]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":574,"outFeatures":471}},{"componentType":"linear","name":"enc2","params":{"inFeatures":471,"outFeatures":186}},{"componentType":"linear","name":"dec1","params":{"inFeatures":186,"outFeatures":471}},{"componentType":"linear","name":"dec2","params":{"inFeatures":471,"outFeatures":574}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":715920}} {"id":"gen-cnn-552","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 91 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-552\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":9737}} {"id":"gen-txf-553","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 234-token sequences with 80 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-553\nComponents:\n - input (input) params={\"shape\":[1,234]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,234]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12373,"embeddingDim":488}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":488,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":488,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7982216}} {"id":"gen-gqa-554","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 509-token sequences with 58 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-554\nComponents:\n - input (input) params={\"shape\":[1,509]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,509]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41777,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4350840}} {"id":"gen-trim-556","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3462-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-556\nComponents:\n - input (input) params={\"shape\":[1,179]}\n - fc1 (linear) params={\"inFeatures\":179,\"outFeatures\":3462}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3462,\"outFeatures\":3462}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3462,\"outFeatures\":3462}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3462,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":179,"outFeatures":311}},{"type":"update_params","name":"fc2","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"fc3","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"head","params":{"inFeatures":311,"outFeatures":19}}],"grade":{"pass":true,"score":84,"params":255020}} {"id":"gen-norm-557","family":"norm","seed":20260716,"spec":"This 137-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-557\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - fc1 (linear) params={\"inFeatures\":137,\"outFeatures\":216}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":216,\"outFeatures\":157}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":157,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":216}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":157}}],"grade":{"pass":true,"score":94,"params":70098}} {"id":"gen-tower-558","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 205-feature user input and a 135-feature item input, each through its own 2-layer MLP tower ending at width 318, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-558\nComponents:\n - user_input (input) params={\"shape\":[1,205]}\n - item_input (input) params={\"shape\":[1,135]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,205]}},{"componentType":"input","name":"item_input","params":{"shape":[1,135]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":205,"outFeatures":318}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":318,"outFeatures":318}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":135,"outFeatures":318}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":318,"outFeatures":318}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":636,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":311004}} {"id":"gen-grow-559","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (119-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-559\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - fc1 (linear) params={\"inFeatures\":119,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":119,"outFeatures":746}},{"type":"update_params","name":"fc2","params":{"inFeatures":746,"outFeatures":746}},{"type":"update_params","name":"head","params":{"inFeatures":746,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":663194}} {"id":"gen-mlp-560","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 126-feature input with 184 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-560\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,126]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":126,"outFeatures":830}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":830,"outFeatures":491}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":491,"outFeatures":97}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":97,"outFeatures":184}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":577585}} {"id":"gen-ae-561","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 302-dim input: encode down to a 71-dim bottleneck and decode back to 302. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-561\nComponents:\n - input (input) params={\"shape\":[1,302]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,302]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":302,"outFeatures":588}},{"componentType":"linear","name":"enc2","params":{"inFeatures":588,"outFeatures":71}},{"componentType":"linear","name":"dec1","params":{"inFeatures":71,"outFeatures":588}},{"componentType":"linear","name":"dec2","params":{"inFeatures":588,"outFeatures":302}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":438648}} {"id":"gen-cnn-562","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 32 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-562\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":30,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":53,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":18976}} {"id":"gen-txf-563","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 393-token sequences with 9 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-563\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24554,"embeddingDim":496}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":496,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":496,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":13167312}} {"id":"gen-gqa-564","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 292-token sequences with 15 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-564\nComponents:\n - input (input) params={\"shape\":[1,292]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,292]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36502,"embeddingDim":76}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":76,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[76]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":76,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[76]}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2775292}} {"id":"gen-trim-566","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3843-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-566\nComponents:\n - input (input) params={\"shape\":[1,248]}\n - fc1 (linear) params={\"inFeatures\":248,\"outFeatures\":3843}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3843,\"outFeatures\":3843}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3843,\"outFeatures\":3843}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3843,\"outFeatures\":3843}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3843,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":248,"outFeatures":260}},{"type":"update_params","name":"fc2","params":{"inFeatures":260,"outFeatures":260}},{"type":"update_params","name":"fc3","params":{"inFeatures":260,"outFeatures":260}},{"type":"update_params","name":"fc4","params":{"inFeatures":260,"outFeatures":260}},{"type":"update_params","name":"head","params":{"inFeatures":260,"outFeatures":21}}],"grade":{"pass":true,"score":88,"params":272740}} {"id":"gen-norm-567","family":"norm","seed":20260716,"spec":"This 96-feature, 64-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-567\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":279}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":279,\"outFeatures\":370}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":370,\"outFeatures\":96}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":96,\"outFeatures\":307}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":307,\"outFeatures\":338}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":338,\"outFeatures\":64}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":279}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":370}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":96}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":307}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":338}}],"grade":{"pass":true,"score":100,"params":320404}} {"id":"gen-tower-568","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 189-feature user input and a 57-feature item input, each through its own 2-layer MLP tower ending at width 492, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-568\nComponents:\n - user_input (input) params={\"shape\":[1,189]}\n - item_input (input) params={\"shape\":[1,57]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,189]}},{"componentType":"input","name":"item_input","params":{"shape":[1,57]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":189,"outFeatures":492}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":492,"outFeatures":492}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":57,"outFeatures":492}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":492,"outFeatures":492}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":984,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":606144}} {"id":"gen-grow-569","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (79-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-569\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - fc1 (linear) params={\"inFeatures\":79,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":79,"outFeatures":1049}},{"type":"update_params","name":"fc2","params":{"inFeatures":1049,"outFeatures":1049}},{"type":"update_params","name":"head","params":{"inFeatures":1049,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":1185370}} {"id":"gen-mlp-570","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 202-feature input with 62 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-570\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,202]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":202,"outFeatures":528}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":528,"outFeatures":79}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":79,"outFeatures":245}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":245,"outFeatures":496}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":496,"outFeatures":457}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":457,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":544249}} {"id":"gen-ae-571","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 332-dim input: encode down to a 103-dim bottleneck and decode back to 332. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-571\nComponents:\n - input (input) params={\"shape\":[1,332]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,332]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":332,"outFeatures":632}},{"componentType":"linear","name":"enc2","params":{"inFeatures":632,"outFeatures":103}},{"componentType":"linear","name":"dec1","params":{"inFeatures":103,"outFeatures":632}},{"componentType":"linear","name":"dec2","params":{"inFeatures":632,"outFeatures":332}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":549840}} {"id":"gen-cnn-572","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 11 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-572\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":44,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":5258}} {"id":"gen-txf-573","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 79-token sequences with 9 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-573\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,79]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33873,"embeddingDim":224}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":224,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":224,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":224,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":8191680}} {"id":"gen-gqa-574","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 70-token sequences with 13 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-574\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,70]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37922,"embeddingDim":144}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":144,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":144,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":144,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[144]}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5462640}} {"id":"gen-trim-576","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3472-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-576\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - fc1 (linear) params={\"inFeatures\":151,\"outFeatures\":3472}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3472,\"outFeatures\":3472}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3472,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":151,"outFeatures":152}},{"type":"update_params","name":"fc2","params":{"inFeatures":152,"outFeatures":152}},{"type":"update_params","name":"head","params":{"inFeatures":152,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":48336}} {"id":"gen-norm-577","family":"norm","seed":20260716,"spec":"This 238-feature, 35-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-577\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - fc1 (linear) params={\"inFeatures\":238,\"outFeatures\":218}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":218,\"outFeatures\":175}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":175,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":218}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":175}}],"grade":{"pass":true,"score":94,"params":96159}} {"id":"gen-tower-578","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 52-feature user input and a 155-feature item input, each through its own 2-layer MLP tower ending at width 74, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-578\nComponents:\n - user_input (input) params={\"shape\":[1,52]}\n - item_input (input) params={\"shape\":[1,155]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,52]}},{"componentType":"input","name":"item_input","params":{"shape":[1,155]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":52,"outFeatures":74}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":74,"outFeatures":74}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":155,"outFeatures":74}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":74,"outFeatures":74}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":148,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":26418}} {"id":"gen-grow-579","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 31 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-579\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":691}},{"type":"update_params","name":"fc2","params":{"inFeatures":691,"outFeatures":691}},{"type":"update_params","name":"head","params":{"inFeatures":691,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":527924}} {"id":"gen-mlp-580","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 496-feature input with 146 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-580\nComponents:\n - input (input) params={\"shape\":[1,496]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,496]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":496,"outFeatures":450}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":450,"outFeatures":593}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":593,"outFeatures":121}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":121,"outFeatures":428}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":428,"outFeatures":559}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":559,"outFeatures":146}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":934457}} {"id":"gen-ae-581","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1310-dim input: encode down to a 28-dim bottleneck and decode back to 1310. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-581\nComponents:\n - input (input) params={\"shape\":[1,1310]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1310]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1310,"outFeatures":247}},{"componentType":"linear","name":"enc2","params":{"inFeatures":247,"outFeatures":28}},{"componentType":"linear","name":"dec1","params":{"inFeatures":28,"outFeatures":247}},{"componentType":"linear","name":"dec2","params":{"inFeatures":247,"outFeatures":1310}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":660972}} {"id":"gen-cnn-582","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 40 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-582\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":33,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":27282}} {"id":"gen-txf-583","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 347-token sequences with 81 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-583\nComponents:\n - input (input) params={\"shape\":[1,347]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,347]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15825,"embeddingDim":464}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":464,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":464,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":8241568}} {"id":"gen-gqa-584","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 298-token sequences with 93 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-584\nComponents:\n - input (input) params={\"shape\":[1,298]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,298]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42457,"embeddingDim":64}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[64]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[64]}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2723200}} {"id":"gen-trim-586","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3570-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-586\nComponents:\n - input (input) params={\"shape\":[1,181]}\n - fc1 (linear) params={\"inFeatures\":181,\"outFeatures\":3570}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3570,\"outFeatures\":3570}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3570,\"outFeatures\":3570}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3570,\"outFeatures\":3570}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3570,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":181,"outFeatures":291}},{"type":"update_params","name":"fc2","params":{"inFeatures":291,"outFeatures":291}},{"type":"update_params","name":"fc3","params":{"inFeatures":291,"outFeatures":291}},{"type":"update_params","name":"fc4","params":{"inFeatures":291,"outFeatures":291}},{"type":"update_params","name":"head","params":{"inFeatures":291,"outFeatures":22}}],"grade":{"pass":true,"score":88,"params":313116}} {"id":"gen-norm-587","family":"norm","seed":20260716,"spec":"This 116-feature, 69-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-587\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":357}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":357,\"outFeatures\":440}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":440,\"outFeatures\":69}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":357}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":440}}],"grade":{"pass":true,"score":94,"params":228852}} {"id":"gen-tower-588","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 123-feature user input and a 69-feature item input, each through its own 2-layer MLP tower ending at width 199, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-588\nComponents:\n - user_input (input) params={\"shape\":[1,123]}\n - item_input (input) params={\"shape\":[1,69]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,123]}},{"componentType":"input","name":"item_input","params":{"shape":[1,69]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":123,"outFeatures":199}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":199,"outFeatures":199}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":69,"outFeatures":199}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":199,"outFeatures":199}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":398,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":117808}} {"id":"gen-grow-589","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-589\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":1129}},{"type":"update_params","name":"fc2","params":{"inFeatures":1129,"outFeatures":1129}},{"type":"update_params","name":"head","params":{"inFeatures":1129,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":1373993}} {"id":"gen-mlp-590","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 285-feature input with 54 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-590\nComponents:\n - input (input) params={\"shape\":[1,285]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,285]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":285,"outFeatures":182}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":182,"outFeatures":699}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":699,"outFeatures":204}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":204,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":332700}} {"id":"gen-ae-591","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1176-dim input: encode down to a 48-dim bottleneck and decode back to 1176. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-591\nComponents:\n - input (input) params={\"shape\":[1,1176]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1176]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1176,"outFeatures":408}},{"componentType":"linear","name":"enc2","params":{"inFeatures":408,"outFeatures":48}},{"componentType":"linear","name":"dec1","params":{"inFeatures":48,"outFeatures":408}},{"componentType":"linear","name":"dec2","params":{"inFeatures":408,"outFeatures":1176}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":998784}} {"id":"gen-cnn-592","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 75 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-592\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":25,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":13980}} {"id":"gen-txf-593","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 304-token sequences with 29 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-593\nComponents:\n - input (input) params={\"shape\":[1,304]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,304]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38369,"embeddingDim":34}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":34,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":34,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1314780}} {"id":"gen-gqa-594","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 394-token sequences with 68 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-594\nComponents:\n - input (input) params={\"shape\":[1,394]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,394]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16433,"embeddingDim":320}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[320]}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5280320}} {"id":"gen-trim-596","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2674-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-596\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - fc1 (linear) params={\"inFeatures\":56,\"outFeatures\":2674}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2674,\"outFeatures\":2674}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2674,\"outFeatures\":2674}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2674,\"outFeatures\":2674}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2674,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":56,"outFeatures":279}},{"type":"update_params","name":"fc2","params":{"inFeatures":279,"outFeatures":279}},{"type":"update_params","name":"fc3","params":{"inFeatures":279,"outFeatures":279}},{"type":"update_params","name":"fc4","params":{"inFeatures":279,"outFeatures":279}},{"type":"update_params","name":"head","params":{"inFeatures":279,"outFeatures":3}}],"grade":{"pass":true,"score":88,"params":249984}} {"id":"gen-norm-597","family":"norm","seed":20260716,"spec":"This 103-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-597\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - fc1 (linear) params={\"inFeatures\":103,\"outFeatures\":63}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":63,\"outFeatures\":322}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":322,\"outFeatures\":506}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":506,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":63}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":322}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":506}}],"grade":{"pass":true,"score":100,"params":206405}} {"id":"gen-tower-598","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 189-feature user input and a 178-feature item input, each through its own 2-layer MLP tower ending at width 312, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-598\nComponents:\n - user_input (input) params={\"shape\":[1,189]}\n - item_input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,189]}},{"componentType":"input","name":"item_input","params":{"shape":[1,178]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":189,"outFeatures":312}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":312,"outFeatures":312}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":178,"outFeatures":312}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":312,"outFeatures":312}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":309816}} {"id":"gen-grow-599","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (83-feature input, 34 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-599\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":83,"outFeatures":1240}},{"type":"update_params","name":"fc2","params":{"inFeatures":1240,"outFeatures":1240}},{"type":"update_params","name":"head","params":{"inFeatures":1240,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":1682680}} {"id":"gen-mlp-600","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 41-feature input with 82 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-600\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,41]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":41,"outFeatures":494}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":494,"outFeatures":229}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":229,"outFeatures":962}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":962,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":432562}} {"id":"gen-ae-601","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1804-dim input: encode down to a 76-dim bottleneck and decode back to 1804. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-601\nComponents:\n - input (input) params={\"shape\":[1,1804]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1804]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1804,"outFeatures":653}},{"componentType":"linear","name":"enc2","params":{"inFeatures":653,"outFeatures":76}},{"componentType":"linear","name":"dec1","params":{"inFeatures":76,"outFeatures":653}},{"componentType":"linear","name":"dec2","params":{"inFeatures":653,"outFeatures":1804}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2455280}} {"id":"gen-cnn-602","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 57 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-602\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":25,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":17,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":8700}} {"id":"gen-txf-603","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 185-token sequences with 13 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-603\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,185]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25664,"embeddingDim":360}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":360,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":9762120}} {"id":"gen-gqa-604","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 82-token sequences with 25 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-604\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,82]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47477,"embeddingDim":232}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":232,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[232]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":232,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[232]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":232,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[232]}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11020464}} {"id":"gen-trim-606","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2147-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-606\nComponents:\n - input (input) params={\"shape\":[1,226]}\n - fc1 (linear) params={\"inFeatures\":226,\"outFeatures\":2147}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2147,\"outFeatures\":2147}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2147,\"outFeatures\":2147}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2147,\"outFeatures\":2147}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2147,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":226,"outFeatures":118}},{"type":"update_params","name":"fc2","params":{"inFeatures":118,"outFeatures":118}},{"type":"update_params","name":"fc3","params":{"inFeatures":118,"outFeatures":118}},{"type":"update_params","name":"fc4","params":{"inFeatures":118,"outFeatures":118}},{"type":"update_params","name":"head","params":{"inFeatures":118,"outFeatures":35}}],"grade":{"pass":true,"score":88,"params":72570}} {"id":"gen-norm-607","family":"norm","seed":20260716,"spec":"This 96-feature, 97-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-607\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":32}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":32,\"outFeatures\":176}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":176,\"outFeatures\":255}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":255,\"outFeatures\":168}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":168,\"outFeatures\":97}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":32}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":176}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":255}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":168}}],"grade":{"pass":true,"score":100,"params":112720}} {"id":"gen-tower-608","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 171-feature user input and a 173-feature item input, each through its own 2-layer MLP tower ending at width 405, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-608\nComponents:\n - user_input (input) params={\"shape\":[1,171]}\n - item_input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,171]}},{"componentType":"input","name":"item_input","params":{"shape":[1,173]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":171,"outFeatures":405}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":405,"outFeatures":405}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":173,"outFeatures":405}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":405,"outFeatures":405}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":810,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":468180}} {"id":"gen-grow-609","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (122-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-609\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":122,"outFeatures":1322}},{"type":"update_params","name":"fc2","params":{"inFeatures":1322,"outFeatures":1322}},{"type":"update_params","name":"head","params":{"inFeatures":1322,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":1938052}} {"id":"gen-mlp-610","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 210-feature input with 164 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-610\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,210]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":210,"outFeatures":958}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":958,"outFeatures":356}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":356,"outFeatures":79}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":79,"outFeatures":62}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":62,"outFeatures":703}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":703,"outFeatures":164}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":734128}} {"id":"gen-ae-611","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1857-dim input: encode down to a 234-dim bottleneck and decode back to 1857. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-611\nComponents:\n - input (input) params={\"shape\":[1,1857]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1857]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1857,"outFeatures":669}},{"componentType":"linear","name":"enc2","params":{"inFeatures":669,"outFeatures":234}},{"componentType":"linear","name":"dec1","params":{"inFeatures":234,"outFeatures":669}},{"componentType":"linear","name":"dec2","params":{"inFeatures":669,"outFeatures":1857}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2797758}} {"id":"gen-cnn-612","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 47 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-612\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7227}} {"id":"gen-txf-613","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 168-token sequences with 96 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-613\nComponents:\n - input (input) params={\"shape\":[1,168]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,168]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34244,"embeddingDim":352}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":352,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":352,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":13078912}} {"id":"gen-gqa-614","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 194-token sequences with 34 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-614\nComponents:\n - input (input) params={\"shape\":[1,194]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,194]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16961,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3127080}} {"id":"gen-trim-616","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3396-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-616\nComponents:\n - input (input) params={\"shape\":[1,189]}\n - fc1 (linear) params={\"inFeatures\":189,\"outFeatures\":3396}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3396,\"outFeatures\":3396}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3396,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":189,"outFeatures":138}},{"type":"update_params","name":"fc2","params":{"inFeatures":138,"outFeatures":138}},{"type":"update_params","name":"head","params":{"inFeatures":138,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":51612}} {"id":"gen-norm-617","family":"norm","seed":20260716,"spec":"This 224-feature, 28-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-617\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":495}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":495,\"outFeatures\":396}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":396,\"outFeatures\":369}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":369,\"outFeatures\":215}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":215,\"outFeatures\":176}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":176,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":495}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":396}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":369}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":215}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":176}}],"grade":{"pass":true,"score":100,"params":575127}} {"id":"gen-tower-618","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 167-feature user input and a 142-feature item input, each through its own 2-layer MLP tower ending at width 325, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-618\nComponents:\n - user_input (input) params={\"shape\":[1,167]}\n - item_input (input) params={\"shape\":[1,142]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,167]}},{"componentType":"input","name":"item_input","params":{"shape":[1,142]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":167,"outFeatures":325}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":142,"outFeatures":325}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":650,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":312325}} {"id":"gen-grow-619","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-619\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":976}},{"type":"update_params","name":"fc2","params":{"inFeatures":976,"outFeatures":976}},{"type":"update_params","name":"head","params":{"inFeatures":976,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":1029680}} {"id":"gen-mlp-620","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 369-feature input with 143 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-620\nComponents:\n - input (input) params={\"shape\":[1,369]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,369]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":369,"outFeatures":887}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":887,"outFeatures":877}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":877,"outFeatures":143}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1230613}} {"id":"gen-ae-621","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1333-dim input: encode down to a 160-dim bottleneck and decode back to 1333. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-621\nComponents:\n - input (input) params={\"shape\":[1,1333]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1333]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1333,"outFeatures":361}},{"componentType":"linear","name":"enc2","params":{"inFeatures":361,"outFeatures":160}},{"componentType":"linear","name":"dec1","params":{"inFeatures":160,"outFeatures":361}},{"componentType":"linear","name":"dec2","params":{"inFeatures":361,"outFeatures":1333}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1077946}} {"id":"gen-cnn-622","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 28 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-622\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":30,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":23894}} {"id":"gen-txf-623","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 187-token sequences with 31 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-623\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,187]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22511,"embeddingDim":192}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":192,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":192,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4622976}} {"id":"gen-gqa-624","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 203-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-624\nComponents:\n - input (input) params={\"shape\":[1,203]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,203]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36541,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":14060544}} {"id":"gen-trim-626","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3925-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-626\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":3925}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3925,\"outFeatures\":3925}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3925,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":48,"outFeatures":212}},{"type":"update_params","name":"fc2","params":{"inFeatures":212,"outFeatures":212}},{"type":"update_params","name":"head","params":{"inFeatures":212,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":63812}} {"id":"gen-norm-627","family":"norm","seed":20260716,"spec":"This 178-feature, 61-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-627\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - fc1 (linear) params={\"inFeatures\":178,\"outFeatures\":32}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":32,\"outFeatures\":278}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":278,\"outFeatures\":258}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":258,\"outFeatures\":246}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":246,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":32}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":278}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":258}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":246}}],"grade":{"pass":true,"score":100,"params":164790}} {"id":"gen-tower-628","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 244-feature user input and a 16-feature item input, each through its own 2-layer MLP tower ending at width 440, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-628\nComponents:\n - user_input (input) params={\"shape\":[1,244]}\n - item_input (input) params={\"shape\":[1,16]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,244]}},{"componentType":"input","name":"item_input","params":{"shape":[1,16]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":244,"outFeatures":440}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":440,"outFeatures":440}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":16,"outFeatures":440}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":440,"outFeatures":440}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":880,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":502480}} {"id":"gen-grow-629","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (66-feature input, 25 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-629\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":66,"outFeatures":979}},{"type":"update_params","name":"fc2","params":{"inFeatures":979,"outFeatures":979}},{"type":"update_params","name":"head","params":{"inFeatures":979,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":1047530}} {"id":"gen-mlp-630","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 133-feature input with 110 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-630\nComponents:\n - input (input) params={\"shape\":[1,133]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,133]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":133,"outFeatures":131}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":131,"outFeatures":302}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":302,"outFeatures":1008}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":1008,"outFeatures":529}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":529,"outFeatures":533}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":533,"outFeatures":110}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1235220}} {"id":"gen-ae-631","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 188-dim input: encode down to a 168-dim bottleneck and decode back to 188. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-631\nComponents:\n - input (input) params={\"shape\":[1,188]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,188]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":188,"outFeatures":309}},{"componentType":"linear","name":"enc2","params":{"inFeatures":309,"outFeatures":168}},{"componentType":"linear","name":"dec1","params":{"inFeatures":168,"outFeatures":309}},{"componentType":"linear","name":"dec2","params":{"inFeatures":309,"outFeatures":188}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":220008}} {"id":"gen-cnn-632","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 93 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-632\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":64,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":38,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":71022}} {"id":"gen-txf-633","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 165-token sequences with 7 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-633\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,165]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42721,"embeddingDim":70}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":70,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":70,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":70,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":70,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3049760}} {"id":"gen-gqa-634","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 62-token sequences with 73 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-634\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,62]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8199,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1058816}} {"id":"gen-trim-636","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2394-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-636\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":2394}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2394,\"outFeatures\":2394}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2394,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":381}},{"type":"update_params","name":"fc2","params":{"inFeatures":381,"outFeatures":381}},{"type":"update_params","name":"head","params":{"inFeatures":381,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":164592}} {"id":"gen-norm-637","family":"norm","seed":20260716,"spec":"This 217-feature, 10-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-637\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":175}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":175,\"outFeatures\":283}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":283,\"outFeatures\":440}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":440,\"outFeatures\":410}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":410,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":175}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":283}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":440}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":410}}],"grade":{"pass":true,"score":100,"params":396520}} {"id":"gen-tower-638","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 220-feature user input and a 184-feature item input, each through its own 2-layer MLP tower ending at width 449, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-638\nComponents:\n - user_input (input) params={\"shape\":[1,220]}\n - item_input (input) params={\"shape\":[1,184]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,220]}},{"componentType":"input","name":"item_input","params":{"shape":[1,184]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":220,"outFeatures":449}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":449,"outFeatures":449}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":184,"outFeatures":449}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":449,"outFeatures":449}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":898,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":585496}} {"id":"gen-grow-639","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (50-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-639\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":853}},{"type":"update_params","name":"fc2","params":{"inFeatures":853,"outFeatures":853}},{"type":"update_params","name":"head","params":{"inFeatures":853,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":789025}} {"id":"gen-mlp-640","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 388-feature input with 90 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-640\nComponents:\n - input (input) params={\"shape\":[1,388]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,388]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":388,"outFeatures":629}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":629,"outFeatures":460}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":460,"outFeatures":512}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":512,"outFeatures":73}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":73,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":812858}} {"id":"gen-ae-641","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 956-dim input: encode down to a 81-dim bottleneck and decode back to 956. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-641\nComponents:\n - input (input) params={\"shape\":[1,956]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,956]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":956,"outFeatures":928}},{"componentType":"linear","name":"enc2","params":{"inFeatures":928,"outFeatures":81}},{"componentType":"linear","name":"dec1","params":{"inFeatures":81,"outFeatures":928}},{"componentType":"linear","name":"dec2","params":{"inFeatures":928,"outFeatures":956}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1924672}} {"id":"gen-cnn-642","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 39 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-642\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":38,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":61,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":57045}} {"id":"gen-txf-643","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 298-token sequences with 63 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-643\nComponents:\n - input (input) params={\"shape\":[1,298]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,298]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49725,"embeddingDim":66}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":66,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":66,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":66,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":66,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3338280}} {"id":"gen-gqa-644","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 352-token sequences with 29 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-644\nComponents:\n - input (input) params={\"shape\":[1,352]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,352]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23467,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":288,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6766848}} {"id":"gen-fix-645","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (368) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-645\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - embed (embedding) params={\"numEmbeddings\":38126,\"embeddingDim\":368}\n - attn (multiHeadAttention) params={\"embedDim\":368,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":368,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":14575008}} {"id":"gen-trim-646","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3952-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-646\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":3952}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3952,\"outFeatures\":3952}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3952,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":148}},{"type":"update_params","name":"fc2","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"head","params":{"inFeatures":148,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":32560}} {"id":"gen-norm-647","family":"norm","seed":20260716,"spec":"This 103-feature, 43-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-647\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - fc1 (linear) params={\"inFeatures\":103,\"outFeatures\":259}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":259,\"outFeatures\":125}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":125,\"outFeatures\":452}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":452,\"outFeatures\":384}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":384,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":259}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":125}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":452}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":384}}],"grade":{"pass":true,"score":100,"params":305632}} {"id":"gen-tower-648","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 86-feature user input and a 109-feature item input, each through its own 2-layer MLP tower ending at width 473, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-648\nComponents:\n - user_input (input) params={\"shape\":[1,86]}\n - item_input (input) params={\"shape\":[1,109]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,86]}},{"componentType":"input","name":"item_input","params":{"shape":[1,109]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":86,"outFeatures":473}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":473,"outFeatures":473}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":109,"outFeatures":473}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":473,"outFeatures":473}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":946,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":540639}} {"id":"gen-grow-649","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (108-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-649\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":727}},{"type":"update_params","name":"fc2","params":{"inFeatures":727,"outFeatures":727}},{"type":"update_params","name":"head","params":{"inFeatures":727,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":640487}} {"id":"gen-mlp-650","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 237-feature input with 39 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-650\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,237]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":237,"outFeatures":494}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":494,"outFeatures":287}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":287,"outFeatures":241}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":241,"outFeatures":68}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":68,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":347063}} {"id":"gen-ae-651","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 101-dim input: encode down to a 98-dim bottleneck and decode back to 101. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-651\nComponents:\n - input (input) params={\"shape\":[1,101]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,101]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":101,"outFeatures":326}},{"componentType":"linear","name":"enc2","params":{"inFeatures":326,"outFeatures":98}},{"componentType":"linear","name":"dec1","params":{"inFeatures":98,"outFeatures":326}},{"componentType":"linear","name":"dec2","params":{"inFeatures":326,"outFeatures":101}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":129748}} {"id":"gen-cnn-652","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 19 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-652\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":14744}} {"id":"gen-txf-653","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 110-token sequences with 41 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-653\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,110]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42688,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":120,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5300280}} {"id":"gen-gqa-654","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 511-token sequences with 34 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-654\nComponents:\n - input (input) params={\"shape\":[1,511]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,511]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31275,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3256136}} {"id":"gen-fix-655","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (268) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-655\nComponents:\n - input (input) params={\"shape\":[1,154]}\n - embed (embedding) params={\"numEmbeddings\":18770,\"embeddingDim\":268}\n - attn (multiHeadAttention) params={\"embedDim\":268,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":268,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":5319800}} {"id":"gen-trim-656","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2419-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-656\nComponents:\n - input (input) params={\"shape\":[1,216]}\n - fc1 (linear) params={\"inFeatures\":216,\"outFeatures\":2419}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2419,\"outFeatures\":2419}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2419,\"outFeatures\":2419}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2419,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":216,"outFeatures":380}},{"type":"update_params","name":"fc2","params":{"inFeatures":380,"outFeatures":380}},{"type":"update_params","name":"fc3","params":{"inFeatures":380,"outFeatures":380}},{"type":"update_params","name":"head","params":{"inFeatures":380,"outFeatures":9}}],"grade":{"pass":true,"score":84,"params":374300}} {"id":"gen-norm-657","family":"norm","seed":20260716,"spec":"This 29-feature, 96-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-657\nComponents:\n - input (input) params={\"shape\":[1,29]}\n - fc1 (linear) params={\"inFeatures\":29,\"outFeatures\":60}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":60,\"outFeatures\":139}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":139,\"outFeatures\":96}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":60}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":139}}],"grade":{"pass":true,"score":94,"params":23424}} {"id":"gen-tower-658","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 256-feature user input and a 246-feature item input, each through its own 2-layer MLP tower ending at width 253, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-658\nComponents:\n - user_input (input) params={\"shape\":[1,256]}\n - item_input (input) params={\"shape\":[1,246]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,256]}},{"componentType":"input","name":"item_input","params":{"shape":[1,246]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":256,"outFeatures":253}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":253,"outFeatures":253}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":246,"outFeatures":253}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":253,"outFeatures":253}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":506,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":255530}} {"id":"gen-grow-659","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (33-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-659\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":651}},{"type":"update_params","name":"fc2","params":{"inFeatures":651,"outFeatures":651}},{"type":"update_params","name":"head","params":{"inFeatures":651,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":448539}} {"id":"gen-mlp-660","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 369-feature input with 32 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-660\nComponents:\n - input (input) params={\"shape\":[1,369]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,369]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":369,"outFeatures":147}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":147,"outFeatures":24}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":24,"outFeatures":771}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":771,"outFeatures":18}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":90729}} {"id":"gen-ae-661","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 221-dim input: encode down to a 46-dim bottleneck and decode back to 221. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-661\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,221]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":221,"outFeatures":632}},{"componentType":"linear","name":"enc2","params":{"inFeatures":632,"outFeatures":46}},{"componentType":"linear","name":"dec1","params":{"inFeatures":46,"outFeatures":632}},{"componentType":"linear","name":"dec2","params":{"inFeatures":632,"outFeatures":221}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":337488}} {"id":"gen-cnn-662","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 63 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-662\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":45,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":39465}} {"id":"gen-txf-663","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 52-token sequences with 97 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-663\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,52]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27947,"embeddingDim":208}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":208,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":208,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":6179264}} {"id":"gen-gqa-664","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 155-token sequences with 93 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-664\nComponents:\n - input (input) params={\"shape\":[1,155]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,155]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22197,"embeddingDim":192}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[192]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[192]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[192]}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4279680}} {"id":"gen-trim-666","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3321-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-666\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - fc1 (linear) params={\"inFeatures\":35,\"outFeatures\":3321}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3321,\"outFeatures\":3321}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3321,\"outFeatures\":3321}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3321,\"outFeatures\":3321}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3321,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":35,"outFeatures":184}},{"type":"update_params","name":"fc2","params":{"inFeatures":184,"outFeatures":184}},{"type":"update_params","name":"fc3","params":{"inFeatures":184,"outFeatures":184}},{"type":"update_params","name":"fc4","params":{"inFeatures":184,"outFeatures":184}},{"type":"update_params","name":"head","params":{"inFeatures":184,"outFeatures":14}}],"grade":{"pass":true,"score":88,"params":110584}} {"id":"gen-norm-667","family":"norm","seed":20260716,"spec":"This 243-feature, 15-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-667\nComponents:\n - input (input) params={\"shape\":[1,243]}\n - fc1 (linear) params={\"inFeatures\":243,\"outFeatures\":171}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":171,\"outFeatures\":444}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":444,\"outFeatures\":199}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":199,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":171}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":444}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":199}}],"grade":{"pass":true,"score":100,"params":208818}} {"id":"gen-tower-668","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 138-feature user input and a 161-feature item input, each through its own 2-layer MLP tower ending at width 431, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-668\nComponents:\n - user_input (input) params={\"shape\":[1,138]}\n - item_input (input) params={\"shape\":[1,161]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,138]}},{"componentType":"input","name":"item_input","params":{"shape":[1,161]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":138,"outFeatures":431}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":431,"outFeatures":431}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":161,"outFeatures":431}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":431,"outFeatures":431}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":862,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":501253}} {"id":"gen-grow-669","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-669\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":1127}},{"type":"update_params","name":"fc2","params":{"inFeatures":1127,"outFeatures":1127}},{"type":"update_params","name":"head","params":{"inFeatures":1127,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":1411004}} {"id":"gen-mlp-670","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 63-feature input with 192 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-670\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,63]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":63,"outFeatures":938}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":938,"outFeatures":1019}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1019,"outFeatures":757}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":757,"outFeatures":65}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":65,"outFeatures":192}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1847984}} {"id":"gen-ae-671","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 124-dim input: encode down to a 220-dim bottleneck and decode back to 124. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-671\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,124]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":124,"outFeatures":845}},{"componentType":"linear","name":"enc2","params":{"inFeatures":845,"outFeatures":220}},{"componentType":"linear","name":"dec1","params":{"inFeatures":220,"outFeatures":845}},{"componentType":"linear","name":"dec2","params":{"inFeatures":845,"outFeatures":124}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":581360}} {"id":"gen-cnn-672","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 13 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-672\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":34,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":26,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37719}} {"id":"gen-txf-673","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 416-token sequences with 2 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-673\nComponents:\n - input (input) params={\"shape\":[1,416]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,416]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15788,"embeddingDim":272}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":272,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5182688}} {"id":"gen-gqa-674","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 512-token sequences with 76 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-674\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31923,"embeddingDim":264}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":264,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[264]}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":8447736}} {"id":"gen-fix-675","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (306) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-675\nComponents:\n - input (input) params={\"shape\":[1,422]}\n - embed (embedding) params={\"numEmbeddings\":12925,\"embeddingDim\":306}\n - attn (multiHeadAttention) params={\"embedDim\":306,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":306,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4343976}} {"id":"gen-trim-676","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 4018-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-676\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":4018}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4018,\"outFeatures\":4018}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4018,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":371}},{"type":"update_params","name":"fc2","params":{"inFeatures":371,"outFeatures":371}},{"type":"update_params","name":"head","params":{"inFeatures":371,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":173628}} {"id":"gen-norm-677","family":"norm","seed":20260716,"spec":"This 209-feature, 26-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-677\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - fc1 (linear) params={\"inFeatures\":209,\"outFeatures\":352}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":352,\"outFeatures\":375}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":375,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":352}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":375}}],"grade":{"pass":true,"score":94,"params":215318}} {"id":"gen-tower-678","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 93-feature user input and a 204-feature item input, each through its own 2-layer MLP tower ending at width 510, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-678\nComponents:\n - user_input (input) params={\"shape\":[1,93]}\n - item_input (input) params={\"shape\":[1,204]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,93]}},{"componentType":"input","name":"item_input","params":{"shape":[1,204]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":93,"outFeatures":510}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":510,"outFeatures":510}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":204,"outFeatures":510}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":510,"outFeatures":510}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1020,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":672690}} {"id":"gen-grow-679","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (121-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-679\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":121,"outFeatures":1011}},{"type":"update_params","name":"fc2","params":{"inFeatures":1011,"outFeatures":1011}},{"type":"update_params","name":"head","params":{"inFeatures":1011,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":1159617}} {"id":"gen-mlp-680","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 369-feature input with 160 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-680\nComponents:\n - input (input) params={\"shape\":[1,369]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,369]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":369,"outFeatures":692}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":692,"outFeatures":215}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":215,"outFeatures":664}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":664,"outFeatures":594}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":594,"outFeatures":75}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":75,"outFeatures":225}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":225,"outFeatures":160}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1038729}} {"id":"gen-ae-681","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 111-dim input: encode down to a 17-dim bottleneck and decode back to 111. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-681\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,111]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":111,"outFeatures":796}},{"componentType":"linear","name":"enc2","params":{"inFeatures":796,"outFeatures":17}},{"componentType":"linear","name":"dec1","params":{"inFeatures":17,"outFeatures":796}},{"componentType":"linear","name":"dec2","params":{"inFeatures":796,"outFeatures":111}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":203776}} {"id":"gen-cnn-682","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 28 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-682\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":6598}} {"id":"gen-txf-683","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 213-token sequences with 79 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-683\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,213]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15940,"embeddingDim":96}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":96,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1574688}} {"id":"gen-gqa-684","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 224-token sequences with 50 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-684\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,224]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26430,"embeddingDim":608}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":608,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[608]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":608,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[608]}},{"componentType":"linear","name":"head","params":{"inFeatures":608,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":16099840}} {"id":"gen-trim-686","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1982-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-686\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":1982}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1982,\"outFeatures\":1982}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1982,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":280}},{"type":"update_params","name":"fc2","params":{"inFeatures":280,"outFeatures":280}},{"type":"update_params","name":"head","params":{"inFeatures":280,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":94920}} {"id":"gen-norm-687","family":"norm","seed":20260716,"spec":"This 138-feature, 77-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-687\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - fc1 (linear) params={\"inFeatures\":138,\"outFeatures\":404}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":404,\"outFeatures\":204}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":204,\"outFeatures\":296}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":296,\"outFeatures\":247}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":247,\"outFeatures\":325}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":325,\"outFeatures\":77}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":404}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":204}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":296}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":247}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":325}}],"grade":{"pass":true,"score":100,"params":376964}} {"id":"gen-tower-688","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 90-feature user input and a 167-feature item input, each through its own 2-layer MLP tower ending at width 479, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-688\nComponents:\n - user_input (input) params={\"shape\":[1,90]}\n - item_input (input) params={\"shape\":[1,167]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,90]}},{"componentType":"input","name":"item_input","params":{"shape":[1,167]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":90,"outFeatures":479}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":479,"outFeatures":479}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":167,"outFeatures":479}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":479,"outFeatures":479}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":958,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":582943}} {"id":"gen-grow-689","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (113-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-689\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - fc1 (linear) params={\"inFeatures\":113,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":113,"outFeatures":1336}},{"type":"update_params","name":"fc2","params":{"inFeatures":1336,"outFeatures":1336}},{"type":"update_params","name":"head","params":{"inFeatures":1336,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":1989304}} {"id":"gen-mlp-690","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 137-feature input with 62 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-690\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,137]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":137,"outFeatures":922}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":922,"outFeatures":690}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":690,"outFeatures":693}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":693,"outFeatures":743}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":743,"outFeatures":530}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":530,"outFeatures":302}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":302,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2328137}} {"id":"gen-ae-691","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 876-dim input: encode down to a 185-dim bottleneck and decode back to 876. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-691\nComponents:\n - input (input) params={\"shape\":[1,876]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,876]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":876,"outFeatures":827}},{"componentType":"linear","name":"enc2","params":{"inFeatures":827,"outFeatures":185}},{"componentType":"linear","name":"dec1","params":{"inFeatures":185,"outFeatures":827}},{"componentType":"linear","name":"dec2","params":{"inFeatures":827,"outFeatures":876}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1754894}} {"id":"gen-cnn-692","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 28 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-692\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":20,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":11148}} {"id":"gen-txf-693","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 391-token sequences with 38 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-693\nComponents:\n - input (input) params={\"shape\":[1,391]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,391]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27580,"embeddingDim":86}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":86,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":86,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":86,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2434316}} {"id":"gen-gqa-694","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 267-token sequences with 13 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-694\nComponents:\n - input (input) params={\"shape\":[1,267]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,267]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37919,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6069120}} {"id":"gen-trim-696","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3914-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-696\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - fc1 (linear) params={\"inFeatures\":100,\"outFeatures\":3914}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3914,\"outFeatures\":3914}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3914,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":100,"outFeatures":321}},{"type":"update_params","name":"fc2","params":{"inFeatures":321,"outFeatures":321}},{"type":"update_params","name":"head","params":{"inFeatures":321,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":150870}} {"id":"gen-norm-697","family":"norm","seed":20260716,"spec":"This 65-feature, 40-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-697\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":484}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":484,\"outFeatures\":432}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":432,\"outFeatures\":236}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":236,\"outFeatures\":271}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":271,\"outFeatures\":257}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":257,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":484}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":432}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":236}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":271}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":257}}],"grade":{"pass":true,"score":100,"params":486383}} {"id":"gen-tower-698","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 195-feature user input and a 141-feature item input, each through its own 2-layer MLP tower ending at width 123, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-698\nComponents:\n - user_input (input) params={\"shape\":[1,195]}\n - item_input (input) params={\"shape\":[1,141]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,195]}},{"componentType":"input","name":"item_input","params":{"shape":[1,141]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":195,"outFeatures":123}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":123,"outFeatures":123}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":141,"outFeatures":123}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":123,"outFeatures":123}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":246,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":71832}} {"id":"gen-grow-699","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (96-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-699\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":96,"outFeatures":764}},{"type":"update_params","name":"fc2","params":{"inFeatures":764,"outFeatures":764}},{"type":"update_params","name":"head","params":{"inFeatures":764,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":690656}} {"id":"gen-mlp-700","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 51-feature input with 34 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-700\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,51]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":51,"outFeatures":917}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":917,"outFeatures":449}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":449,"outFeatures":865}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":865,"outFeatures":162}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":162,"outFeatures":189}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":189,"outFeatures":149}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":149,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1050860}} {"id":"gen-ae-701","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1779-dim input: encode down to a 211-dim bottleneck and decode back to 1779. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-701\nComponents:\n - input (input) params={\"shape\":[1,1779]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1779]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1779,"outFeatures":555}},{"componentType":"linear","name":"enc2","params":{"inFeatures":555,"outFeatures":211}},{"componentType":"linear","name":"dec1","params":{"inFeatures":211,"outFeatures":555}},{"componentType":"linear","name":"dec2","params":{"inFeatures":555,"outFeatures":1779}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2208900}} {"id":"gen-cnn-702","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 90 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-702\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":25,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":18774}} {"id":"gen-txf-703","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 356-token sequences with 32 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-703\nComponents:\n - input (input) params={\"shape\":[1,356]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,356]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39880,"embeddingDim":236}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":236,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":236,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":236,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":236,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":10087584}} {"id":"gen-gqa-704","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 139-token sequences with 65 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-704\nComponents:\n - input (input) params={\"shape\":[1,139]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,139]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34650,"embeddingDim":688}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":688,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[688]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":688,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[688]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":688,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[688]}},{"componentType":"linear","name":"head","params":{"inFeatures":688,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":23883920}} {"id":"gen-trim-706","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2367-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-706\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - fc1 (linear) params={\"inFeatures\":183,\"outFeatures\":2367}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2367,\"outFeatures\":2367}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2367,\"outFeatures\":2367}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2367,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":183,"outFeatures":203}},{"type":"update_params","name":"fc2","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"fc3","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"head","params":{"inFeatures":203,"outFeatures":38}}],"grade":{"pass":true,"score":84,"params":127281}} {"id":"gen-norm-707","family":"norm","seed":20260716,"spec":"This 33-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-707\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":310}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":310,\"outFeatures\":153}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":153,\"outFeatures\":201}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":201,\"outFeatures\":370}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":370,\"outFeatures\":218}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":218,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":310}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":153}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":201}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":370}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":218}}],"grade":{"pass":true,"score":100,"params":251727}} {"id":"gen-tower-708","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 239-feature user input and a 51-feature item input, each through its own 2-layer MLP tower ending at width 51, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-708\nComponents:\n - user_input (input) params={\"shape\":[1,239]}\n - item_input (input) params={\"shape\":[1,51]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,239]}},{"componentType":"input","name":"item_input","params":{"shape":[1,51]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":239,"outFeatures":51}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":102,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":20094}} {"id":"gen-grow-709","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (70-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-709\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":70,"outFeatures":721}},{"type":"update_params","name":"fc2","params":{"inFeatures":721,"outFeatures":721}},{"type":"update_params","name":"head","params":{"inFeatures":721,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":602035}} {"id":"gen-mlp-710","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 184-feature input with 122 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-710\nComponents:\n - input (input) params={\"shape\":[1,184]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,184]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":184,"outFeatures":452}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":452,"outFeatures":168}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":168,"outFeatures":647}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":647,"outFeatures":122}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":346734}} {"id":"gen-ae-711","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 115-dim input: encode down to a 139-dim bottleneck and decode back to 115. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-711\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,115]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":115,"outFeatures":579}},{"componentType":"linear","name":"enc2","params":{"inFeatures":579,"outFeatures":139}},{"componentType":"linear","name":"dec1","params":{"inFeatures":139,"outFeatures":579}},{"componentType":"linear","name":"dec2","params":{"inFeatures":579,"outFeatures":115}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":294132}} {"id":"gen-cnn-712","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 31 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-712\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":29,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":34,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":22126}} {"id":"gen-txf-713","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 65-token sequences with 26 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-713\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,65]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38065,"embeddingDim":110}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":110,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":110,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":110,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4335210}} {"id":"gen-gqa-714","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 205-token sequences with 59 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-714\nComponents:\n - input (input) params={\"shape\":[1,205]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,205]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28075,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4951584}} {"id":"gen-trim-716","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1637-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-716\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":1637}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1637,\"outFeatures\":1637}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1637,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":69}},{"type":"update_params","name":"fc2","params":{"inFeatures":69,"outFeatures":69}},{"type":"update_params","name":"head","params":{"inFeatures":69,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":8142}} {"id":"gen-norm-717","family":"norm","seed":20260716,"spec":"This 198-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-717\nComponents:\n - input (input) params={\"shape\":[1,198]}\n - fc1 (linear) params={\"inFeatures\":198,\"outFeatures\":436}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":436,\"outFeatures\":56}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":56,\"outFeatures\":461}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":461,\"outFeatures\":293}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":293,\"outFeatures\":62}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":62,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":436}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":56}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":461}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":293}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":62}}],"grade":{"pass":true,"score":100,"params":293519}} {"id":"gen-tower-718","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 92-feature user input and a 54-feature item input, each through its own 2-layer MLP tower ending at width 295, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-718\nComponents:\n - user_input (input) params={\"shape\":[1,92]}\n - item_input (input) params={\"shape\":[1,54]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,92]}},{"componentType":"input","name":"item_input","params":{"shape":[1,54]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":92,"outFeatures":295}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":295,"outFeatures":295}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":54,"outFeatures":295}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":295,"outFeatures":295}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":590,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":217710}} {"id":"gen-grow-719","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (80-feature input, 47 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-719\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":80,"outFeatures":833}},{"type":"update_params","name":"fc2","params":{"inFeatures":833,"outFeatures":833}},{"type":"update_params","name":"head","params":{"inFeatures":833,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":799680}} {"id":"gen-mlp-720","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 462-feature input with 128 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-720\nComponents:\n - input (input) params={\"shape\":[1,462]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,462]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":462,"outFeatures":422}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":422,"outFeatures":1019}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1019,"outFeatures":65}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":65,"outFeatures":433}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":433,"outFeatures":128}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":774786}} {"id":"gen-ae-721","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 135-dim input: encode down to a 247-dim bottleneck and decode back to 135. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-721\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,135]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":135,"outFeatures":203}},{"componentType":"linear","name":"enc2","params":{"inFeatures":203,"outFeatures":247}},{"componentType":"linear","name":"dec1","params":{"inFeatures":247,"outFeatures":203}},{"componentType":"linear","name":"dec2","params":{"inFeatures":203,"outFeatures":135}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":155092}} {"id":"gen-cnn-722","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 52 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-722\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":36,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":26,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":39,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39478}} {"id":"gen-txf-723","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 383-token sequences with 45 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-723\nComponents:\n - input (input) params={\"shape\":[1,383]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,383]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34253,"embeddingDim":28}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":28,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":28,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":28,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":969752}} {"id":"gen-gqa-724","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 113-token sequences with 53 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-724\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,113]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31331,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":17575040}} {"id":"gen-trim-726","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3971-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-726\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":3971}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3971,\"outFeatures\":3971}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3971,\"outFeatures\":3971}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3971,\"outFeatures\":3971}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3971,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":139}},{"type":"update_params","name":"fc2","params":{"inFeatures":139,"outFeatures":139}},{"type":"update_params","name":"fc3","params":{"inFeatures":139,"outFeatures":139}},{"type":"update_params","name":"fc4","params":{"inFeatures":139,"outFeatures":139}},{"type":"update_params","name":"head","params":{"inFeatures":139,"outFeatures":8}}],"grade":{"pass":true,"score":88,"params":64913}} {"id":"gen-norm-727","family":"norm","seed":20260716,"spec":"This 145-feature, 29-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-727\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":102}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":102,\"outFeatures\":286}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":286,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":102}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":286}}],"grade":{"pass":true,"score":94,"params":52256}} {"id":"gen-tower-728","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 132-feature user input and a 84-feature item input, each through its own 2-layer MLP tower ending at width 481, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-728\nComponents:\n - user_input (input) params={\"shape\":[1,132]}\n - item_input (input) params={\"shape\":[1,84]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,132]}},{"componentType":"input","name":"item_input","params":{"shape":[1,84]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":132,"outFeatures":481}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":481,"outFeatures":481}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":84,"outFeatures":481}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":481,"outFeatures":481}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":962,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":567580}} {"id":"gen-grow-729","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (98-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-729\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":98,"outFeatures":863}},{"type":"update_params","name":"fc2","params":{"inFeatures":863,"outFeatures":863}},{"type":"update_params","name":"head","params":{"inFeatures":863,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":846603}} {"id":"gen-mlp-730","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 154-feature input with 75 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-730\nComponents:\n - input (input) params={\"shape\":[1,154]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,154]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":154,"outFeatures":569}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":569,"outFeatures":173}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":173,"outFeatures":54}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":54,"outFeatures":490}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":490,"outFeatures":213}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":213,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":342210}} {"id":"gen-ae-731","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 144-dim input: encode down to a 79-dim bottleneck and decode back to 144. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-731\nComponents:\n - input (input) params={\"shape\":[1,144]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,144]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":144,"outFeatures":551}},{"componentType":"linear","name":"enc2","params":{"inFeatures":551,"outFeatures":79}},{"componentType":"linear","name":"dec1","params":{"inFeatures":79,"outFeatures":551}},{"componentType":"linear","name":"dec2","params":{"inFeatures":551,"outFeatures":144}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":245746}} {"id":"gen-cnn-732","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 59 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-732\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":29,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":23,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":12,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":17954}} {"id":"gen-txf-733","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 114-token sequences with 24 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-733\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,114]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31255,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1510608}} {"id":"gen-gqa-734","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 229-token sequences with 72 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-734\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,229]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35494,"embeddingDim":448}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":448,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[448]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":448,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[448]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":448,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[448]}},{"componentType":"linear","name":"head","params":{"inFeatures":448,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":15933568}} {"id":"gen-trim-736","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1683-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-736\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - fc1 (linear) params={\"inFeatures\":56,\"outFeatures\":1683}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1683,\"outFeatures\":1683}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1683,\"outFeatures\":1683}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1683,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":56,"outFeatures":151}},{"type":"update_params","name":"fc2","params":{"inFeatures":151,"outFeatures":151}},{"type":"update_params","name":"fc3","params":{"inFeatures":151,"outFeatures":151}},{"type":"update_params","name":"head","params":{"inFeatures":151,"outFeatures":36}}],"grade":{"pass":true,"score":84,"params":59494}} {"id":"gen-norm-737","family":"norm","seed":20260716,"spec":"This 108-feature, 52-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-737\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":126}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":126,\"outFeatures\":327}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":327,\"outFeatures\":277}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":277,\"outFeatures\":144}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":144,\"outFeatures\":510}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":510,\"outFeatures\":52}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":126}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":327}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":277}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":144}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":510}}],"grade":{"pass":true,"score":100,"params":285237}} {"id":"gen-tower-738","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 112-feature user input and a 205-feature item input, each through its own 2-layer MLP tower ending at width 151, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-738\nComponents:\n - user_input (input) params={\"shape\":[1,112]}\n - item_input (input) params={\"shape\":[1,205]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,112]}},{"componentType":"input","name":"item_input","params":{"shape":[1,205]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":112,"outFeatures":151}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":205,"outFeatures":151}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":302,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":93771}} {"id":"gen-grow-739","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (29-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-739\nComponents:\n - input (input) params={\"shape\":[1,29]}\n - fc1 (linear) params={\"inFeatures\":29,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":29,"outFeatures":909}},{"type":"update_params","name":"fc2","params":{"inFeatures":909,"outFeatures":909}},{"type":"update_params","name":"head","params":{"inFeatures":909,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":862641}} {"id":"gen-mlp-740","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 393-feature input with 81 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-740\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":393,"outFeatures":744}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":744,"outFeatures":979}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":979,"outFeatures":933}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":933,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":2009748}} {"id":"gen-ae-741","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1947-dim input: encode down to a 176-dim bottleneck and decode back to 1947. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-741\nComponents:\n - input (input) params={\"shape\":[1,1947]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1947]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1947,"outFeatures":740}},{"componentType":"linear","name":"enc2","params":{"inFeatures":740,"outFeatures":176}},{"componentType":"linear","name":"dec1","params":{"inFeatures":176,"outFeatures":740}},{"componentType":"linear","name":"dec2","params":{"inFeatures":740,"outFeatures":1947}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3142040}} {"id":"gen-cnn-742","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 88 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-742\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":49,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":13490}} {"id":"gen-txf-743","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 429-token sequences with 45 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-743\nComponents:\n - input (input) params={\"shape\":[1,429]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,429]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36746,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2669688}} {"id":"gen-gqa-744","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 229-token sequences with 91 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-744\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,229]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29611,"embeddingDim":464}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":464,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[464]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":464,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[464]}},{"componentType":"linear","name":"head","params":{"inFeatures":464,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":13781728}} {"id":"gen-trim-746","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3439-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-746\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - fc1 (linear) params={\"inFeatures\":253,\"outFeatures\":3439}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3439,\"outFeatures\":3439}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3439,\"outFeatures\":3439}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3439,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":253,"outFeatures":164}},{"type":"update_params","name":"fc2","params":{"inFeatures":164,"outFeatures":164}},{"type":"update_params","name":"fc3","params":{"inFeatures":164,"outFeatures":164}},{"type":"update_params","name":"head","params":{"inFeatures":164,"outFeatures":21}}],"grade":{"pass":true,"score":84,"params":98728}} {"id":"gen-norm-747","family":"norm","seed":20260716,"spec":"This 199-feature, 56-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-747\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - fc1 (linear) params={\"inFeatures\":199,\"outFeatures\":498}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":498,\"outFeatures\":369}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":369,\"outFeatures\":364}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":364,\"outFeatures\":406}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":406,\"outFeatures\":56}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":498}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":369}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":364}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":406}}],"grade":{"pass":true,"score":100,"params":587700}} {"id":"gen-tower-748","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 200-feature user input and a 107-feature item input, each through its own 2-layer MLP tower ending at width 66, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-748\nComponents:\n - user_input (input) params={\"shape\":[1,200]}\n - item_input (input) params={\"shape\":[1,107]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,200]}},{"componentType":"input","name":"item_input","params":{"shape":[1,107]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":200,"outFeatures":66}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":66,"outFeatures":66}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":107,"outFeatures":66}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":66,"outFeatures":66}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":132,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29106}} {"id":"gen-grow-749","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (75-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-749\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":75,"outFeatures":971}},{"type":"update_params","name":"fc2","params":{"inFeatures":971,"outFeatures":971}},{"type":"update_params","name":"head","params":{"inFeatures":971,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":1056448}} {"id":"gen-mlp-750","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 34-feature input with 137 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-750\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,34]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":34,"outFeatures":649}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":649,"outFeatures":610}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":610,"outFeatures":961}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":961,"outFeatures":137}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1135823}} {"id":"gen-ae-751","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 173-dim input: encode down to a 184-dim bottleneck and decode back to 173. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-751\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,173]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":173,"outFeatures":751}},{"componentType":"linear","name":"enc2","params":{"inFeatures":751,"outFeatures":184}},{"componentType":"linear","name":"dec1","params":{"inFeatures":184,"outFeatures":751}},{"componentType":"linear","name":"dec2","params":{"inFeatures":751,"outFeatures":173}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":536214}} {"id":"gen-cnn-752","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 38 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-752\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":52,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":32270}} {"id":"gen-txf-753","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 174-token sequences with 87 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-753\nComponents:\n - input (input) params={\"shape\":[1,174]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,174]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19312,"embeddingDim":236}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":236,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":236,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":236,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":236,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5246516}} {"id":"gen-gqa-754","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 346-token sequences with 10 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-754\nComponents:\n - input (input) params={\"shape\":[1,346]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,346]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39395,"embeddingDim":40}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[40]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[40]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[40]}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1576200}} {"id":"gen-fix-755","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (98) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-755\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - embed (embedding) params={\"numEmbeddings\":18544,\"embeddingDim\":98}\n - attn (multiHeadAttention) params={\"embedDim\":98,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":98,\"outFeatures\":64}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":1862000}} {"id":"gen-trim-756","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1578-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-756\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":1578}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1578,\"outFeatures\":1578}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1578,\"outFeatures\":1578}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1578,\"outFeatures\":1578}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1578,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":126,"outFeatures":357}},{"type":"update_params","name":"fc2","params":{"inFeatures":357,"outFeatures":357}},{"type":"update_params","name":"fc3","params":{"inFeatures":357,"outFeatures":357}},{"type":"update_params","name":"fc4","params":{"inFeatures":357,"outFeatures":357}},{"type":"update_params","name":"head","params":{"inFeatures":357,"outFeatures":18}}],"grade":{"pass":true,"score":88,"params":433755}} {"id":"gen-norm-757","family":"norm","seed":20260716,"spec":"This 161-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-757\nComponents:\n - input (input) params={\"shape\":[1,161]}\n - fc1 (linear) params={\"inFeatures\":161,\"outFeatures\":182}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":182,\"outFeatures\":93}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":93,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":182}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":93}}],"grade":{"pass":true,"score":94,"params":50134}} {"id":"gen-tower-758","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 233-feature user input and a 143-feature item input, each through its own 2-layer MLP tower ending at width 112, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-758\nComponents:\n - user_input (input) params={\"shape\":[1,233]}\n - item_input (input) params={\"shape\":[1,143]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,233]}},{"componentType":"input","name":"item_input","params":{"shape":[1,143]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":233,"outFeatures":112}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":112,"outFeatures":112}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":143,"outFeatures":112}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":112,"outFeatures":112}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":67424}} {"id":"gen-grow-759","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (103-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-759\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - fc1 (linear) params={\"inFeatures\":103,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":103,"outFeatures":1259}},{"type":"update_params","name":"fc2","params":{"inFeatures":1259,"outFeatures":1259}},{"type":"update_params","name":"head","params":{"inFeatures":1259,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":1767636}} {"id":"gen-mlp-760","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 423-feature input with 91 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-760\nComponents:\n - input (input) params={\"shape\":[1,423]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,423]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":423,"outFeatures":923}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":923,"outFeatures":636}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":636,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1035333}} {"id":"gen-ae-761","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1384-dim input: encode down to a 234-dim bottleneck and decode back to 1384. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-761\nComponents:\n - input (input) params={\"shape\":[1,1384]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1384]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1384,"outFeatures":203}},{"componentType":"linear","name":"enc2","params":{"inFeatures":203,"outFeatures":234}},{"componentType":"linear","name":"dec1","params":{"inFeatures":234,"outFeatures":203}},{"componentType":"linear","name":"dec2","params":{"inFeatures":203,"outFeatures":1384}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":656908}} {"id":"gen-cnn-762","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 5 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-762\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":20,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":34,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":20382}} {"id":"gen-txf-763","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 424-token sequences with 34 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-763\nComponents:\n - input (input) params={\"shape\":[1,424]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,424]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47047,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":88,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4236056}} {"id":"gen-gqa-764","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 348-token sequences with 81 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-764\nComponents:\n - input (input) params={\"shape\":[1,348]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,348]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47418,"embeddingDim":76}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":76,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[76]}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3609924}} {"id":"gen-fix-765","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (226) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-765\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - embed (embedding) params={\"numEmbeddings\":33802,\"embeddingDim\":226}\n - attn (multiHeadAttention) params={\"embedDim\":226,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":226,\"outFeatures\":69}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":7859150}} {"id":"gen-trim-766","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2018-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-766\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - fc1 (linear) params={\"inFeatures\":141,\"outFeatures\":2018}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2018,\"outFeatures\":2018}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2018,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":141,"outFeatures":194}},{"type":"update_params","name":"fc2","params":{"inFeatures":194,"outFeatures":194}},{"type":"update_params","name":"head","params":{"inFeatures":194,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":69064}} {"id":"gen-norm-767","family":"norm","seed":20260716,"spec":"This 142-feature, 2-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-767\nComponents:\n - input (input) params={\"shape\":[1,142]}\n - fc1 (linear) params={\"inFeatures\":142,\"outFeatures\":448}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":448,\"outFeatures\":76}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":76,\"outFeatures\":438}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":438,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":448}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":76}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":438}}],"grade":{"pass":true,"score":100,"params":131828}} {"id":"gen-tower-768","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 243-feature user input and a 175-feature item input, each through its own 2-layer MLP tower ending at width 288, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-768\nComponents:\n - user_input (input) params={\"shape\":[1,243]}\n - item_input (input) params={\"shape\":[1,175]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,243]}},{"componentType":"input","name":"item_input","params":{"shape":[1,175]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":243,"outFeatures":288}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":175,"outFeatures":288}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":576,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":286848}} {"id":"gen-grow-769","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (58-feature input, 45 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-769\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - fc1 (linear) params={\"inFeatures\":58,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":58,"outFeatures":1333}},{"type":"update_params","name":"fc2","params":{"inFeatures":1333,"outFeatures":1333}},{"type":"update_params","name":"head","params":{"inFeatures":1333,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":1914188}} {"id":"gen-mlp-770","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 202-feature input with 72 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-770\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,202]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":202,"outFeatures":640}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":640,"outFeatures":610}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":610,"outFeatures":751}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":751,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1031862}} {"id":"gen-ae-771","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 343-dim input: encode down to a 240-dim bottleneck and decode back to 343. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-771\nComponents:\n - input (input) params={\"shape\":[1,343]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,343]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":343,"outFeatures":381}},{"componentType":"linear","name":"enc2","params":{"inFeatures":381,"outFeatures":240}},{"componentType":"linear","name":"dec1","params":{"inFeatures":240,"outFeatures":381}},{"componentType":"linear","name":"dec2","params":{"inFeatures":381,"outFeatures":343}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":444246}} {"id":"gen-cnn-772","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 80 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-772\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":35,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30968}} {"id":"gen-txf-773","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 437-token sequences with 11 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-773\nComponents:\n - input (input) params={\"shape\":[1,437]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,437]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30809,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":176,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":176,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5796032}} {"id":"gen-gqa-774","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 423-token sequences with 76 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-774\nComponents:\n - input (input) params={\"shape\":[1,423]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,423]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47121,"embeddingDim":40}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[40]}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1887880}} {"id":"gen-fix-775","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (268) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-775\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - embed (embedding) params={\"numEmbeddings\":15965,\"embeddingDim\":268}\n - attn (multiHeadAttention) params={\"embedDim\":268,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":268,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":4571812}} {"id":"gen-trim-776","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2817-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-776\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - fc1 (linear) params={\"inFeatures\":214,\"outFeatures\":2817}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2817,\"outFeatures\":2817}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2817,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":214,"outFeatures":66}},{"type":"update_params","name":"fc2","params":{"inFeatures":66,"outFeatures":66}},{"type":"update_params","name":"head","params":{"inFeatures":66,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":21054}} {"id":"gen-norm-777","family":"norm","seed":20260716,"spec":"This 256-feature, 12-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-777\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - fc1 (linear) params={\"inFeatures\":256,\"outFeatures\":62}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":62,\"outFeatures\":343}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":343,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":62}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":343}}],"grade":{"pass":true,"score":94,"params":41254}} {"id":"gen-tower-778","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 231-feature user input and a 177-feature item input, each through its own 2-layer MLP tower ending at width 375, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-778\nComponents:\n - user_input (input) params={\"shape\":[1,231]}\n - item_input (input) params={\"shape\":[1,177]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,231]}},{"componentType":"input","name":"item_input","params":{"shape":[1,177]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":231,"outFeatures":375}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":177,"outFeatures":375}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":750,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":435000}} {"id":"gen-grow-779","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (36-feature input, 25 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-779\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - fc1 (linear) params={\"inFeatures\":36,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":36,"outFeatures":1373}},{"type":"update_params","name":"fc2","params":{"inFeatures":1373,"outFeatures":1373}},{"type":"update_params","name":"head","params":{"inFeatures":1373,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":1968882}} {"id":"gen-mlp-780","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 186-feature input with 146 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-780\nComponents:\n - input (input) params={\"shape\":[1,186]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,186]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":186,"outFeatures":981}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":981,"outFeatures":297}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":297,"outFeatures":146}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":517185}} {"id":"gen-ae-781","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 672-dim input: encode down to a 44-dim bottleneck and decode back to 672. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-781\nComponents:\n - input (input) params={\"shape\":[1,672]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,672]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":672,"outFeatures":847}},{"componentType":"linear","name":"enc2","params":{"inFeatures":847,"outFeatures":44}},{"componentType":"linear","name":"dec1","params":{"inFeatures":44,"outFeatures":847}},{"componentType":"linear","name":"dec2","params":{"inFeatures":847,"outFeatures":672}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1212904}} {"id":"gen-cnn-782","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 15 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-782\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":49,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":11811}} {"id":"gen-txf-783","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 87-token sequences with 69 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-783\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,87]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9816,"embeddingDim":244}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":244,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":244,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":244,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2888228}} {"id":"gen-gqa-784","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 251-token sequences with 48 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-784\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,251]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35959,"embeddingDim":312}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":312,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[312]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":312,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[312]}},{"componentType":"linear","name":"head","params":{"inFeatures":312,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":11234184}} {"id":"gen-trim-786","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3012-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-786\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - fc1 (linear) params={\"inFeatures\":230,\"outFeatures\":3012}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3012,\"outFeatures\":3012}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3012,\"outFeatures\":3012}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3012,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":230,"outFeatures":76}},{"type":"update_params","name":"fc2","params":{"inFeatures":76,"outFeatures":76}},{"type":"update_params","name":"fc3","params":{"inFeatures":76,"outFeatures":76}},{"type":"update_params","name":"head","params":{"inFeatures":76,"outFeatures":7}}],"grade":{"pass":true,"score":84,"params":29564}} {"id":"gen-norm-787","family":"norm","seed":20260716,"spec":"This 114-feature, 80-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-787\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":50}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":50,\"outFeatures\":424}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":424,\"outFeatures\":231}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":231,\"outFeatures\":383}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":383,\"outFeatures\":80}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":50}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":424}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":231}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":383}}],"grade":{"pass":true,"score":100,"params":243957}} {"id":"gen-tower-788","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 255-feature user input and a 186-feature item input, each through its own 2-layer MLP tower ending at width 212, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-788\nComponents:\n - user_input (input) params={\"shape\":[1,255]}\n - item_input (input) params={\"shape\":[1,186]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,255]}},{"componentType":"input","name":"item_input","params":{"shape":[1,186]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":255,"outFeatures":212}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":212,"outFeatures":212}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":186,"outFeatures":212}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":212,"outFeatures":212}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":424,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":183804}} {"id":"gen-grow-789","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (48-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-789\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":48,"outFeatures":687}},{"type":"update_params","name":"fc2","params":{"inFeatures":687,"outFeatures":687}},{"type":"update_params","name":"head","params":{"inFeatures":687,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":509754}} {"id":"gen-mlp-790","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 55-feature input with 42 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-790\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,55]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":55,"outFeatures":634}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":634,"outFeatures":967}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":967,"outFeatures":982}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":982,"outFeatures":437}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":437,"outFeatures":663}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":663,"outFeatures":513}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":513,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2678072}} {"id":"gen-ae-791","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 238-dim input: encode down to a 90-dim bottleneck and decode back to 238. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-791\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,238]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":238,"outFeatures":953}},{"componentType":"linear","name":"enc2","params":{"inFeatures":953,"outFeatures":90}},{"componentType":"linear","name":"dec1","params":{"inFeatures":90,"outFeatures":953}},{"componentType":"linear","name":"dec2","params":{"inFeatures":953,"outFeatures":238}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":625168}} {"id":"gen-cnn-792","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 54 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-792\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":53,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":51039}} {"id":"gen-txf-793","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 123-token sequences with 72 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-793\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,123]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15754,"embeddingDim":64}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":64,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1029248}} {"id":"gen-gqa-794","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 400-token sequences with 3 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-794\nComponents:\n - input (input) params={\"shape\":[1,400]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,400]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43355,"embeddingDim":640}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":640,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":640,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[640]}},{"componentType":"linear","name":"head","params":{"inFeatures":640,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":27749120}} {"id":"gen-fix-795","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (414) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-795\nComponents:\n - input (input) params={\"shape\":[1,316]}\n - embed (embedding) params={\"numEmbeddings\":48413,\"embeddingDim\":414}\n - attn (multiHeadAttention) params={\"embedDim\":414,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":414,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":20734362}} {"id":"gen-trim-796","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 4048-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-796\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - fc1 (linear) params={\"inFeatures\":170,\"outFeatures\":4048}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4048,\"outFeatures\":4048}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4048,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":170,"outFeatures":130}},{"type":"update_params","name":"fc2","params":{"inFeatures":130,"outFeatures":130}},{"type":"update_params","name":"head","params":{"inFeatures":130,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":42250}} {"id":"gen-norm-797","family":"norm","seed":20260716,"spec":"This 241-feature, 48-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-797\nComponents:\n - input (input) params={\"shape\":[1,241]}\n - fc1 (linear) params={\"inFeatures\":241,\"outFeatures\":458}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":458,\"outFeatures\":168}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":168,\"outFeatures\":426}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":426,\"outFeatures\":398}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":398,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":458}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":168}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":426}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":398}}],"grade":{"pass":true,"score":100,"params":447542}} {"id":"gen-tower-798","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 148-feature user input and a 202-feature item input, each through its own 2-layer MLP tower ending at width 208, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-798\nComponents:\n - user_input (input) params={\"shape\":[1,148]}\n - item_input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,148]}},{"componentType":"input","name":"item_input","params":{"shape":[1,202]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":148,"outFeatures":208}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":208,"outFeatures":208}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":202,"outFeatures":208}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":208,"outFeatures":208}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":159744}} {"id":"gen-grow-799","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-799\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":1000}},{"type":"update_params","name":"fc2","params":{"inFeatures":1000,"outFeatures":1000}},{"type":"update_params","name":"head","params":{"inFeatures":1000,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1050000}} {"id":"gen-mlp-800","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 22-feature input with 189 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-800\nComponents:\n - input (input) params={\"shape\":[1,22]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,22]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":22,"outFeatures":313}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":313,"outFeatures":939}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":939,"outFeatures":189}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":478264}} {"id":"gen-ae-801","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1162-dim input: encode down to a 224-dim bottleneck and decode back to 1162. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-801\nComponents:\n - input (input) params={\"shape\":[1,1162]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1162]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1162,"outFeatures":592}},{"componentType":"linear","name":"enc2","params":{"inFeatures":592,"outFeatures":224}},{"componentType":"linear","name":"dec1","params":{"inFeatures":224,"outFeatures":592}},{"componentType":"linear","name":"dec2","params":{"inFeatures":592,"outFeatures":1162}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1641024}} {"id":"gen-cnn-802","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 9 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-802\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":55,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":49959}} {"id":"gen-txf-803","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 296-token sequences with 4 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-803\nComponents:\n - input (input) params={\"shape\":[1,296]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,296]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44039,"embeddingDim":432}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":432,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":432,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":432,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":21266064}} {"id":"gen-gqa-804","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 173-token sequences with 85 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-804\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,173]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43266,"embeddingDim":100}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[100]}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4335100}} {"id":"gen-fix-805","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (298) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-805\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - embed (embedding) params={\"numEmbeddings\":28752,\"embeddingDim\":298}\n - attn (multiHeadAttention) params={\"embedDim\":298,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":298,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":8929868}} {"id":"gen-trim-806","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2595-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-806\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":2595}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2595,\"outFeatures\":2595}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2595,\"outFeatures\":2595}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2595,\"outFeatures\":2595}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2595,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":53,"outFeatures":220}},{"type":"update_params","name":"fc2","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"fc3","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"fc4","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"head","params":{"inFeatures":220,"outFeatures":47}}],"grade":{"pass":true,"score":88,"params":167200}} {"id":"gen-norm-807","family":"norm","seed":20260716,"spec":"This 143-feature, 32-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-807\nComponents:\n - input (input) params={\"shape\":[1,143]}\n - fc1 (linear) params={\"inFeatures\":143,\"outFeatures\":426}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":426,\"outFeatures\":109}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":109,\"outFeatures\":94}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":94,\"outFeatures\":168}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":168,\"outFeatures\":380}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":380,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":426}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":109}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":94}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":168}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":380}}],"grade":{"pass":true,"score":100,"params":209390}} {"id":"gen-tower-808","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 200-feature user input and a 79-feature item input, each through its own 2-layer MLP tower ending at width 254, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-808\nComponents:\n - user_input (input) params={\"shape\":[1,200]}\n - item_input (input) params={\"shape\":[1,79]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,200]}},{"componentType":"input","name":"item_input","params":{"shape":[1,79]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":200,"outFeatures":254}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":254,"outFeatures":254}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":79,"outFeatures":254}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":254,"outFeatures":254}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":508,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":200406}} {"id":"gen-grow-809","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-809\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":753}},{"type":"update_params","name":"fc2","params":{"inFeatures":753,"outFeatures":753}},{"type":"update_params","name":"head","params":{"inFeatures":753,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":629508}} {"id":"gen-mlp-810","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 458-feature input with 79 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-810\nComponents:\n - input (input) params={\"shape\":[1,458]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,458]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":458,"outFeatures":780}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":780,"outFeatures":233}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":233,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":557387}} {"id":"gen-ae-811","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1847-dim input: encode down to a 16-dim bottleneck and decode back to 1847. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-811\nComponents:\n - input (input) params={\"shape\":[1,1847]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1847]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1847,"outFeatures":171}},{"componentType":"linear","name":"enc2","params":{"inFeatures":171,"outFeatures":16}},{"componentType":"linear","name":"dec1","params":{"inFeatures":16,"outFeatures":171}},{"componentType":"linear","name":"dec2","params":{"inFeatures":171,"outFeatures":1847}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":637146}} {"id":"gen-cnn-812","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 96 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-812\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":60,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":43,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":42813}} {"id":"gen-txf-813","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 138-token sequences with 69 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-813\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,138]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35028,"embeddingDim":272}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":272,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":10138256}} {"id":"gen-gqa-814","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 197-token sequences with 84 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-814\nComponents:\n - input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,197]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19705,"embeddingDim":64}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[64]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[64]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[64]}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":84}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1266496}} {"id":"gen-trim-816","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2760-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-816\nComponents:\n - input (input) params={\"shape\":[1,176]}\n - fc1 (linear) params={\"inFeatures\":176,\"outFeatures\":2760}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2760,\"outFeatures\":2760}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2760,\"outFeatures\":2760}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2760,\"outFeatures\":2760}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2760,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":176,"outFeatures":210}},{"type":"update_params","name":"fc2","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"fc3","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"fc4","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"head","params":{"inFeatures":210,"outFeatures":23}}],"grade":{"pass":true,"score":88,"params":174090}} {"id":"gen-norm-817","family":"norm","seed":20260716,"spec":"This 185-feature, 80-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-817\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - fc1 (linear) params={\"inFeatures\":185,\"outFeatures\":111}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":111,\"outFeatures\":268}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":268,\"outFeatures\":213}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":213,\"outFeatures\":54}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":54,\"outFeatures\":404}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":404,\"outFeatures\":80}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":111}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":268}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":213}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":54}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":404}}],"grade":{"pass":true,"score":100,"params":173005}} {"id":"gen-tower-818","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 160-feature user input and a 123-feature item input, each through its own 2-layer MLP tower ending at width 177, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-818\nComponents:\n - user_input (input) params={\"shape\":[1,160]}\n - item_input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,160]}},{"componentType":"input","name":"item_input","params":{"shape":[1,123]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":160,"outFeatures":177}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":177,"outFeatures":177}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":123,"outFeatures":177}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":177,"outFeatures":177}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":354,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":113103}} {"id":"gen-grow-819","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (83-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-819\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":83,"outFeatures":1332}},{"type":"update_params","name":"fc2","params":{"inFeatures":1332,"outFeatures":1332}},{"type":"update_params","name":"head","params":{"inFeatures":1332,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":1931400}} {"id":"gen-mlp-820","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 186-feature input with 179 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-820\nComponents:\n - input (input) params={\"shape\":[1,186]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,186]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":186,"outFeatures":933}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":933,"outFeatures":755}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":755,"outFeatures":846}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":846,"outFeatures":788}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":788,"outFeatures":179}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":2324383}} {"id":"gen-ae-821","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 224-dim input: encode down to a 168-dim bottleneck and decode back to 224. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-821\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,224]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":224,"outFeatures":482}},{"componentType":"linear","name":"enc2","params":{"inFeatures":482,"outFeatures":168}},{"componentType":"linear","name":"dec1","params":{"inFeatures":168,"outFeatures":482}},{"componentType":"linear","name":"dec2","params":{"inFeatures":482,"outFeatures":224}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":377888}} {"id":"gen-cnn-822","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 94 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-822\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":24,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":11278}} {"id":"gen-txf-823","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 128-token sequences with 10 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-823\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16650,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1348152}} {"id":"gen-gqa-824","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 442-token sequences with 13 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-824\nComponents:\n - input (input) params={\"shape\":[1,442]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,442]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44650,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":12862944}} {"id":"gen-fix-825","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (460) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-825\nComponents:\n - input (input) params={\"shape\":[1,508]}\n - embed (embedding) params={\"numEmbeddings\":21848,\"embeddingDim\":460}\n - attn (multiHeadAttention) params={\"embedDim\":460,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":460,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":10917640}} {"id":"gen-trim-826","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3551-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-826\nComponents:\n - input (input) params={\"shape\":[1,153]}\n - fc1 (linear) params={\"inFeatures\":153,\"outFeatures\":3551}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3551,\"outFeatures\":3551}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3551,\"outFeatures\":3551}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3551,\"outFeatures\":3551}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3551,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":153,"outFeatures":79}},{"type":"update_params","name":"fc2","params":{"inFeatures":79,"outFeatures":79}},{"type":"update_params","name":"fc3","params":{"inFeatures":79,"outFeatures":79}},{"type":"update_params","name":"fc4","params":{"inFeatures":79,"outFeatures":79}},{"type":"update_params","name":"head","params":{"inFeatures":79,"outFeatures":26}}],"grade":{"pass":true,"score":88,"params":32864}} {"id":"gen-norm-827","family":"norm","seed":20260716,"spec":"This 74-feature, 77-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-827\nComponents:\n - input (input) params={\"shape\":[1,74]}\n - fc1 (linear) params={\"inFeatures\":74,\"outFeatures\":220}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":220,\"outFeatures\":149}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":149,\"outFeatures\":487}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":487,\"outFeatures\":77}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":220}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":149}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":487}}],"grade":{"pass":true,"score":100,"params":159122}} {"id":"gen-tower-828","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 90-feature user input and a 103-feature item input, each through its own 2-layer MLP tower ending at width 51, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-828\nComponents:\n - user_input (input) params={\"shape\":[1,90]}\n - item_input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,90]}},{"componentType":"input","name":"item_input","params":{"shape":[1,103]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":90,"outFeatures":51}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":103,"outFeatures":51}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":102,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":15147}} {"id":"gen-grow-829","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (34-feature input, 23 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-829\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - fc1 (linear) params={\"inFeatures\":34,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":34,"outFeatures":814}},{"type":"update_params","name":"fc2","params":{"inFeatures":814,"outFeatures":814}},{"type":"update_params","name":"head","params":{"inFeatures":814,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":708994}} {"id":"gen-mlp-830","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 40-feature input with 123 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-830\nComponents:\n - input (input) params={\"shape\":[1,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,40]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":40,"outFeatures":180}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":180,"outFeatures":952}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":952,"outFeatures":134}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":134,"outFeatures":280}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":280,"outFeatures":264}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":123}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":450040}} {"id":"gen-ae-831","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1260-dim input: encode down to a 41-dim bottleneck and decode back to 1260. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-831\nComponents:\n - input (input) params={\"shape\":[1,1260]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1260]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1260,"outFeatures":527}},{"componentType":"linear","name":"enc2","params":{"inFeatures":527,"outFeatures":41}},{"componentType":"linear","name":"dec1","params":{"inFeatures":41,"outFeatures":527}},{"componentType":"linear","name":"dec2","params":{"inFeatures":527,"outFeatures":1260}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1371254}} {"id":"gen-cnn-832","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 36 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-832\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":40,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":59,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":50,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":50670}} {"id":"gen-txf-833","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 397-token sequences with 69 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-833\nComponents:\n - input (input) params={\"shape\":[1,397]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,397]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39433,"embeddingDim":512}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":512,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":512,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":512,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":512,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":23370752}} {"id":"gen-gqa-834","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 209-token sequences with 66 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-834\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,209]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25690,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6593536}} {"id":"gen-trim-836","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3311-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-836\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":3311}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3311,\"outFeatures\":3311}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3311,\"outFeatures\":3311}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3311,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":70,"outFeatures":92}},{"type":"update_params","name":"fc2","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"fc3","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"head","params":{"inFeatures":92,"outFeatures":36}}],"grade":{"pass":true,"score":84,"params":26680}} {"id":"gen-norm-837","family":"norm","seed":20260716,"spec":"This 88-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-837\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":132}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":132,\"outFeatures\":309}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":309,\"outFeatures\":467}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":467,\"outFeatures\":140}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":140,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":132}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":309}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":467}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":140}}],"grade":{"pass":true,"score":100,"params":266287}} {"id":"gen-tower-838","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 250-feature user input and a 103-feature item input, each through its own 2-layer MLP tower ending at width 62, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-838\nComponents:\n - user_input (input) params={\"shape\":[1,250]}\n - item_input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,250]}},{"componentType":"input","name":"item_input","params":{"shape":[1,103]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":250,"outFeatures":62}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":103,"outFeatures":62}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29698}} {"id":"gen-grow-839","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (128-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-839\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":128,"outFeatures":1210}},{"type":"update_params","name":"fc2","params":{"inFeatures":1210,"outFeatures":1210}},{"type":"update_params","name":"head","params":{"inFeatures":1210,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":1627450}} {"id":"gen-mlp-840","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 88-feature input with 58 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-840\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,88]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":88,"outFeatures":438}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":438,"outFeatures":887}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":887,"outFeatures":309}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":309,"outFeatures":943}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":943,"outFeatures":917}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":917,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1910437}} {"id":"gen-ae-841","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 295-dim input: encode down to a 66-dim bottleneck and decode back to 295. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-841\nComponents:\n - input (input) params={\"shape\":[1,295]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,295]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":295,"outFeatures":119}},{"componentType":"linear","name":"enc2","params":{"inFeatures":119,"outFeatures":66}},{"componentType":"linear","name":"dec1","params":{"inFeatures":66,"outFeatures":119}},{"componentType":"linear","name":"dec2","params":{"inFeatures":119,"outFeatures":295}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":85918}} {"id":"gen-cnn-842","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x26x26 image with 4 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-842\nComponents:\n - input (input) params={\"shape\":[1,3,26,26]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,26,26]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":32,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":18384}} {"id":"gen-txf-843","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 223-token sequences with 16 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-843\nComponents:\n - input (input) params={\"shape\":[1,223]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,223]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35522,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3739216}} {"id":"gen-gqa-844","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 227-token sequences with 89 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-844\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15958,"embeddingDim":656}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":656,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[656]}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":10526832}} {"id":"gen-trim-846","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1649-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-846\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":1649}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1649,\"outFeatures\":1649}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1649,\"outFeatures\":1649}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1649,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":75}},{"type":"update_params","name":"fc2","params":{"inFeatures":75,"outFeatures":75}},{"type":"update_params","name":"fc3","params":{"inFeatures":75,"outFeatures":75}},{"type":"update_params","name":"head","params":{"inFeatures":75,"outFeatures":5}}],"grade":{"pass":true,"score":84,"params":20175}} {"id":"gen-norm-847","family":"norm","seed":20260716,"spec":"This 36-feature, 28-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-847\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - fc1 (linear) params={\"inFeatures\":36,\"outFeatures\":475}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":475,\"outFeatures\":231}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":231,\"outFeatures\":489}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":489,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":475}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":231}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":489}}],"grade":{"pass":true,"score":100,"params":253476}} {"id":"gen-tower-848","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 165-feature item input, each through its own 2-layer MLP tower ending at width 157, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-848\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,165]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,165]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":157}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":157,"outFeatures":157}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":165,"outFeatures":157}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":157,"outFeatures":157}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":314,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":113197}} {"id":"gen-grow-849","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (69-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-849\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - fc1 (linear) params={\"inFeatures\":69,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":69,"outFeatures":1353}},{"type":"update_params","name":"fc2","params":{"inFeatures":1353,"outFeatures":1353}},{"type":"update_params","name":"head","params":{"inFeatures":1353,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":1948320}} {"id":"gen-mlp-850","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 88-feature input with 66 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-850\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,88]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":88,"outFeatures":600}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":600,"outFeatures":381}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":381,"outFeatures":465}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":465,"outFeatures":205}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":205,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":567420}} {"id":"gen-ae-851","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1752-dim input: encode down to a 118-dim bottleneck and decode back to 1752. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-851\nComponents:\n - input (input) params={\"shape\":[1,1752]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1752]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1752,"outFeatures":874}},{"componentType":"linear","name":"enc2","params":{"inFeatures":874,"outFeatures":118}},{"componentType":"linear","name":"dec1","params":{"inFeatures":118,"outFeatures":874}},{"componentType":"linear","name":"dec2","params":{"inFeatures":874,"outFeatures":1752}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3268760}} {"id":"gen-cnn-852","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 43 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-852\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":34595}} {"id":"gen-txf-853","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 278-token sequences with 45 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-853\nComponents:\n - input (input) params={\"shape\":[1,278]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,278]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22202,"embeddingDim":76}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":76,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":76,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":76,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1760084}} {"id":"gen-gqa-854","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 488-token sequences with 17 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-854\nComponents:\n - input (input) params={\"shape\":[1,488]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,488]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14094,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1354656}} {"id":"gen-fix-855","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (266) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-855\nComponents:\n - input (input) params={\"shape\":[1,286]}\n - embed (embedding) params={\"numEmbeddings\":16229,\"embeddingDim\":266}\n - attn (multiHeadAttention) params={\"embedDim\":266,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":266,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4610312}} {"id":"gen-trim-856","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2527-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-856\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":2527}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2527,\"outFeatures\":2527}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2527,\"outFeatures\":2527}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2527,\"outFeatures\":2527}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2527,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":80,"outFeatures":66}},{"type":"update_params","name":"fc2","params":{"inFeatures":66,"outFeatures":66}},{"type":"update_params","name":"fc3","params":{"inFeatures":66,"outFeatures":66}},{"type":"update_params","name":"fc4","params":{"inFeatures":66,"outFeatures":66}},{"type":"update_params","name":"head","params":{"inFeatures":66,"outFeatures":20}}],"grade":{"pass":true,"score":88,"params":19668}} {"id":"gen-norm-857","family":"norm","seed":20260716,"spec":"This 57-feature, 81-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-857\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":84}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":84,\"outFeatures\":118}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":118,\"outFeatures\":93}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":93,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":84}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":118}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":93}}],"grade":{"pass":true,"score":100,"params":33207}} {"id":"gen-tower-858","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 86-feature user input and a 88-feature item input, each through its own 2-layer MLP tower ending at width 340, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-858\nComponents:\n - user_input (input) params={\"shape\":[1,86]}\n - item_input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,86]}},{"componentType":"input","name":"item_input","params":{"shape":[1,88]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":86,"outFeatures":340}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":340,"outFeatures":340}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":88,"outFeatures":340}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":340,"outFeatures":340}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":680,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":291040}} {"id":"gen-grow-859","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (118-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-859\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":118,"outFeatures":1092}},{"type":"update_params","name":"fc2","params":{"inFeatures":1092,"outFeatures":1092}},{"type":"update_params","name":"head","params":{"inFeatures":1092,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":1369368}} {"id":"gen-mlp-860","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 275-feature input with 131 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-860\nComponents:\n - input (input) params={\"shape\":[1,275]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,275]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":275,"outFeatures":23}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":23,"outFeatures":950}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":950,"outFeatures":258}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":258,"outFeatures":688}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":688,"outFeatures":50}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":50,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":491729}} {"id":"gen-ae-861","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 850-dim input: encode down to a 166-dim bottleneck and decode back to 850. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-861\nComponents:\n - input (input) params={\"shape\":[1,850]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,850]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":850,"outFeatures":232}},{"componentType":"linear","name":"enc2","params":{"inFeatures":232,"outFeatures":166}},{"componentType":"linear","name":"dec1","params":{"inFeatures":166,"outFeatures":232}},{"componentType":"linear","name":"dec2","params":{"inFeatures":232,"outFeatures":850}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":471424}} {"id":"gen-cnn-862","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 90 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-862\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":49,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":22563}} {"id":"gen-txf-863","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 262-token sequences with 19 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-863\nComponents:\n - input (input) params={\"shape\":[1,262]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,262]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34160,"embeddingDim":280}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":280,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":280,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":280,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":10197320}} {"id":"gen-gqa-864","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 289-token sequences with 53 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-864\nComponents:\n - input (input) params={\"shape\":[1,289]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,289]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48174,"embeddingDim":208}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":208,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[208]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":208,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[208]}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":10031216}} {"id":"gen-trim-866","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1571-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-866\nComponents:\n - input (input) params={\"shape\":[1,241]}\n - fc1 (linear) params={\"inFeatures\":241,\"outFeatures\":1571}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1571,\"outFeatures\":1571}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1571,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":241,"outFeatures":198}},{"type":"update_params","name":"fc2","params":{"inFeatures":198,"outFeatures":198}},{"type":"update_params","name":"head","params":{"inFeatures":198,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":93456}} {"id":"gen-norm-867","family":"norm","seed":20260716,"spec":"This 107-feature, 27-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-867\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":213}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":213,\"outFeatures\":425}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":425,\"outFeatures\":414}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":414,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":213}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":425}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":414}}],"grade":{"pass":true,"score":100,"params":300444}} {"id":"gen-tower-868","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 159-feature user input and a 76-feature item input, each through its own 2-layer MLP tower ending at width 297, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-868\nComponents:\n - user_input (input) params={\"shape\":[1,159]}\n - item_input (input) params={\"shape\":[1,76]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,159]}},{"componentType":"input","name":"item_input","params":{"shape":[1,76]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":159,"outFeatures":297}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":76,"outFeatures":297}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":594,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":246807}} {"id":"gen-grow-869","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (115-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-869\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":767}},{"type":"update_params","name":"fc2","params":{"inFeatures":767,"outFeatures":767}},{"type":"update_params","name":"head","params":{"inFeatures":767,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":701805}} {"id":"gen-mlp-870","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 228-feature input with 100 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-870\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":228,"outFeatures":990}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":990,"outFeatures":1000}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1000,"outFeatures":419}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":419,"outFeatures":68}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":68,"outFeatures":155}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":155,"outFeatures":644}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":644,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1837972}} {"id":"gen-ae-871","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 735-dim input: encode down to a 153-dim bottleneck and decode back to 735. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-871\nComponents:\n - input (input) params={\"shape\":[1,735]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,735]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":735,"outFeatures":456}},{"componentType":"linear","name":"enc2","params":{"inFeatures":456,"outFeatures":153}},{"componentType":"linear","name":"dec1","params":{"inFeatures":153,"outFeatures":456}},{"componentType":"linear","name":"dec2","params":{"inFeatures":456,"outFeatures":735}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":809856}} {"id":"gen-cnn-872","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 80 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-872\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":40,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":15,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7680}} {"id":"gen-txf-873","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 233-token sequences with 12 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-873\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,233]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22436,"embeddingDim":126}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":126,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":126,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":126,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2955456}} {"id":"gen-gqa-874","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 500-token sequences with 33 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-874\nComponents:\n - input (input) params={\"shape\":[1,500]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,500]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43455,"embeddingDim":640}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":640,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":640,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[640]}},{"componentType":"linear","name":"head","params":{"inFeatures":640,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":27832320}} {"id":"gen-fix-875","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (328) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-875\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - embed (embedding) params={\"numEmbeddings\":15054,\"embeddingDim\":328}\n - attn (multiHeadAttention) params={\"embedDim\":328,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":328,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":5382152}} {"id":"gen-trim-876","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2984-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-876\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":2984}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2984,\"outFeatures\":2984}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2984,\"outFeatures\":2984}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2984,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":245}},{"type":"update_params","name":"fc2","params":{"inFeatures":245,"outFeatures":245}},{"type":"update_params","name":"fc3","params":{"inFeatures":245,"outFeatures":245}},{"type":"update_params","name":"head","params":{"inFeatures":245,"outFeatures":12}}],"grade":{"pass":true,"score":84,"params":151655}} {"id":"gen-norm-877","family":"norm","seed":20260716,"spec":"This 81-feature, 65-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-877\nComponents:\n - input (input) params={\"shape\":[1,81]}\n - fc1 (linear) params={\"inFeatures\":81,\"outFeatures\":70}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":70,\"outFeatures\":324}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":324,\"outFeatures\":488}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":488,\"outFeatures\":48}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":48,\"outFeatures\":330}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":330,\"outFeatures\":65}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":70}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":324}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":488}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":48}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":330}}],"grade":{"pass":true,"score":100,"params":247176}} {"id":"gen-tower-878","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 118-feature user input and a 128-feature item input, each through its own 2-layer MLP tower ending at width 297, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-878\nComponents:\n - user_input (input) params={\"shape\":[1,118]}\n - item_input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,118]}},{"componentType":"input","name":"item_input","params":{"shape":[1,128]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":118,"outFeatures":297}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":128,"outFeatures":297}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":594,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":250074}} {"id":"gen-grow-879","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (102-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-879\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - fc1 (linear) params={\"inFeatures\":102,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":102,"outFeatures":1110}},{"type":"update_params","name":"fc2","params":{"inFeatures":1110,"outFeatures":1110}},{"type":"update_params","name":"head","params":{"inFeatures":1110,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":1371960}} {"id":"gen-mlp-880","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 158-feature input with 83 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-880\nComponents:\n - input (input) params={\"shape\":[1,158]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,158]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":158,"outFeatures":213}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":213,"outFeatures":213}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":213,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":96702}} {"id":"gen-ae-881","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1858-dim input: encode down to a 82-dim bottleneck and decode back to 1858. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-881\nComponents:\n - input (input) params={\"shape\":[1,1858]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1858]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1858,"outFeatures":696}},{"componentType":"linear","name":"enc2","params":{"inFeatures":696,"outFeatures":82}},{"componentType":"linear","name":"dec1","params":{"inFeatures":82,"outFeatures":696}},{"componentType":"linear","name":"dec2","params":{"inFeatures":696,"outFeatures":1858}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2700480}} {"id":"gen-cnn-882","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 68 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-882\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":28,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":58,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46353}} {"id":"gen-txf-883","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 253-token sequences with 79 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-883\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,253]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43178,"embeddingDim":256}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":256,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":11335936}} {"id":"gen-gqa-884","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 499-token sequences with 12 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-884\nComponents:\n - input (input) params={\"shape\":[1,499]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,499]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18397,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4712704}} {"id":"gen-fix-885","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (266) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-885\nComponents:\n - input (input) params={\"shape\":[1,248]}\n - embed (embedding) params={\"numEmbeddings\":45272,\"embeddingDim\":266}\n - attn (multiHeadAttention) params={\"embedDim\":266,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":266,\"outFeatures\":52}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":12339208}} {"id":"gen-trim-886","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2829-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-886\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - fc1 (linear) params={\"inFeatures\":208,\"outFeatures\":2829}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2829,\"outFeatures\":2829}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2829,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":208,"outFeatures":231}},{"type":"update_params","name":"fc2","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"head","params":{"inFeatures":231,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":104643}} {"id":"gen-norm-887","family":"norm","seed":20260716,"spec":"This 129-feature, 80-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-887\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - fc1 (linear) params={\"inFeatures\":129,\"outFeatures\":362}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":362,\"outFeatures\":428}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":428,\"outFeatures\":224}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":224,\"outFeatures\":80}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":362}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":428}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":224}}],"grade":{"pass":true,"score":100,"params":315426}} {"id":"gen-tower-888","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 169-feature user input and a 134-feature item input, each through its own 2-layer MLP tower ending at width 193, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-888\nComponents:\n - user_input (input) params={\"shape\":[1,169]}\n - item_input (input) params={\"shape\":[1,134]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,169]}},{"componentType":"input","name":"item_input","params":{"shape":[1,134]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":169,"outFeatures":193}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":193,"outFeatures":193}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":134,"outFeatures":193}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":193,"outFeatures":193}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":386,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":133363}} {"id":"gen-grow-889","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 10 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-889\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":981}},{"type":"update_params","name":"fc2","params":{"inFeatures":981,"outFeatures":981}},{"type":"update_params","name":"head","params":{"inFeatures":981,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":1057518}} {"id":"gen-mlp-890","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 177-feature input with 73 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-890\nComponents:\n - input (input) params={\"shape\":[1,177]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,177]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":177,"outFeatures":706}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":706,"outFeatures":906}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":906,"outFeatures":1003}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1003,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1746535}} {"id":"gen-ae-891","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 787-dim input: encode down to a 217-dim bottleneck and decode back to 787. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-891\nComponents:\n - input (input) params={\"shape\":[1,787]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,787]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":787,"outFeatures":838}},{"componentType":"linear","name":"enc2","params":{"inFeatures":838,"outFeatures":217}},{"componentType":"linear","name":"dec1","params":{"inFeatures":217,"outFeatures":838}},{"componentType":"linear","name":"dec2","params":{"inFeatures":838,"outFeatures":787}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1682704}} {"id":"gen-cnn-892","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 95 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-892\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":36,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":13,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":7499}} {"id":"gen-txf-893","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 206-token sequences with 70 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-893\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,206]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17020,"embeddingDim":212}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":212,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":212,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":212,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":212,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4162408}} {"id":"gen-gqa-894","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 298-token sequences with 9 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-894\nComponents:\n - input (input) params={\"shape\":[1,298]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,298]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18545,"embeddingDim":172}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[172]}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3191288}} {"id":"gen-trim-896","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2094-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-896\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":2094}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2094,\"outFeatures\":2094}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2094,\"outFeatures\":2094}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2094,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":53,"outFeatures":268}},{"type":"update_params","name":"fc2","params":{"inFeatures":268,"outFeatures":268}},{"type":"update_params","name":"fc3","params":{"inFeatures":268,"outFeatures":268}},{"type":"update_params","name":"head","params":{"inFeatures":268,"outFeatures":4}}],"grade":{"pass":true,"score":84,"params":158924}} {"id":"gen-norm-897","family":"norm","seed":20260716,"spec":"This 41-feature, 32-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-897\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":132}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":132,\"outFeatures\":407}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":407,\"outFeatures\":197}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":197,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":132}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":407}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":197}}],"grade":{"pass":true,"score":100,"params":145619}} {"id":"gen-tower-898","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 112-feature user input and a 255-feature item input, each through its own 2-layer MLP tower ending at width 95, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-898\nComponents:\n - user_input (input) params={\"shape\":[1,112]}\n - item_input (input) params={\"shape\":[1,255]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,112]}},{"componentType":"input","name":"item_input","params":{"shape":[1,255]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":112,"outFeatures":95}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":95,"outFeatures":95}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":255,"outFeatures":95}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":95,"outFeatures":95}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":190,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":53105}} {"id":"gen-grow-899","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (17-feature input, 30 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-899\nComponents:\n - input (input) params={\"shape\":[1,17]}\n - fc1 (linear) params={\"inFeatures\":17,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":17,"outFeatures":792}},{"type":"update_params","name":"fc2","params":{"inFeatures":792,"outFeatures":792}},{"type":"update_params","name":"head","params":{"inFeatures":792,"outFeatures":30}}],"grade":{"pass":true,"score":80,"params":664488}} {"id":"gen-mlp-900","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 408-feature input with 118 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-900\nComponents:\n - input (input) params={\"shape\":[1,408]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,408]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":408,"outFeatures":718}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":718,"outFeatures":1018}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1018,"outFeatures":311}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":311,"outFeatures":570}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":570,"outFeatures":118}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1584996}} {"id":"gen-ae-901","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2000-dim input: encode down to a 29-dim bottleneck and decode back to 2000. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-901\nComponents:\n - input (input) params={\"shape\":[1,2000]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2000]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2000,"outFeatures":638}},{"componentType":"linear","name":"enc2","params":{"inFeatures":638,"outFeatures":29}},{"componentType":"linear","name":"dec1","params":{"inFeatures":29,"outFeatures":638}},{"componentType":"linear","name":"dec2","params":{"inFeatures":638,"outFeatures":2000}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2589004}} {"id":"gen-cnn-902","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 38 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-902\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":47,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":20119}} {"id":"gen-txf-903","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 114-token sequences with 90 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-903\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,114]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48733,"embeddingDim":82}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":82,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":82,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4084174}} {"id":"gen-gqa-904","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 289-token sequences with 41 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-904\nComponents:\n - input (input) params={\"shape\":[1,289]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,289]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33649,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5929440}} {"id":"gen-trim-906","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2384-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-906\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":2384}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2384,\"outFeatures\":2384}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2384,\"outFeatures\":2384}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2384,\"outFeatures\":2384}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2384,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":161}},{"type":"update_params","name":"fc2","params":{"inFeatures":161,"outFeatures":161}},{"type":"update_params","name":"fc3","params":{"inFeatures":161,"outFeatures":161}},{"type":"update_params","name":"fc4","params":{"inFeatures":161,"outFeatures":161}},{"type":"update_params","name":"head","params":{"inFeatures":161,"outFeatures":12}}],"grade":{"pass":true,"score":88,"params":86779}} {"id":"gen-norm-907","family":"norm","seed":20260716,"spec":"This 60-feature, 21-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-907\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":336}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":336,\"outFeatures\":158}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":158,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":336}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":158}}],"grade":{"pass":true,"score":94,"params":76566}} {"id":"gen-tower-908","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 54-feature user input and a 37-feature item input, each through its own 2-layer MLP tower ending at width 257, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-908\nComponents:\n - user_input (input) params={\"shape\":[1,54]}\n - item_input (input) params={\"shape\":[1,37]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,54]}},{"componentType":"input","name":"item_input","params":{"shape":[1,37]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":54,"outFeatures":257}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":257,"outFeatures":257}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":37,"outFeatures":257}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":257,"outFeatures":257}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":514,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":155999}} {"id":"gen-grow-909","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (18-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-909\nComponents:\n - input (input) params={\"shape\":[1,18]}\n - fc1 (linear) params={\"inFeatures\":18,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":18,"outFeatures":707}},{"type":"update_params","name":"fc2","params":{"inFeatures":707,"outFeatures":707}},{"type":"update_params","name":"head","params":{"inFeatures":707,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":513989}} {"id":"gen-mlp-910","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 143-feature input with 52 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-910\nComponents:\n - input (input) params={\"shape\":[1,143]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,143]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":143,"outFeatures":76}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":76,"outFeatures":373}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":373,"outFeatures":550}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":550,"outFeatures":911}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":911,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":792788}} {"id":"gen-ae-911","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1992-dim input: encode down to a 148-dim bottleneck and decode back to 1992. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-911\nComponents:\n - input (input) params={\"shape\":[1,1992]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1992]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1992,"outFeatures":503}},{"componentType":"linear","name":"enc2","params":{"inFeatures":503,"outFeatures":148}},{"componentType":"linear","name":"dec1","params":{"inFeatures":148,"outFeatures":503}},{"componentType":"linear","name":"dec2","params":{"inFeatures":503,"outFeatures":1992}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2152840}} {"id":"gen-cnn-912","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 86 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-912\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":29,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":33,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":22418}} {"id":"gen-txf-913","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 431-token sequences with 87 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-913\nComponents:\n - input (input) params={\"shape\":[1,431]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,431]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14744,"embeddingDim":68}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":68,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":68,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1027004}} {"id":"gen-gqa-914","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 47-token sequences with 7 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-914\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,47]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17429,"embeddingDim":92}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[92]}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1604112}} {"id":"gen-fix-915","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (156) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-915\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - embed (embedding) params={\"numEmbeddings\":25737,\"embeddingDim\":156}\n - attn (multiHeadAttention) params={\"embedDim\":156,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":156,\"outFeatures\":89}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":4126200}} {"id":"gen-trim-916","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2570-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-916\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":2570}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2570,\"outFeatures\":2570}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2570,\"outFeatures\":2570}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2570,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":127,"outFeatures":383}},{"type":"update_params","name":"fc2","params":{"inFeatures":383,"outFeatures":383}},{"type":"update_params","name":"fc3","params":{"inFeatures":383,"outFeatures":383}},{"type":"update_params","name":"head","params":{"inFeatures":383,"outFeatures":32}}],"grade":{"pass":true,"score":84,"params":354275}} {"id":"gen-norm-917","family":"norm","seed":20260716,"spec":"This 166-feature, 49-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-917\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - fc1 (linear) params={\"inFeatures\":166,\"outFeatures\":473}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":473,\"outFeatures\":153}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":153,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":473}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":153}}],"grade":{"pass":true,"score":94,"params":158384}} {"id":"gen-tower-918","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 217-feature user input and a 26-feature item input, each through its own 2-layer MLP tower ending at width 183, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-918\nComponents:\n - user_input (input) params={\"shape\":[1,217]}\n - item_input (input) params={\"shape\":[1,26]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,217]}},{"componentType":"input","name":"item_input","params":{"shape":[1,26]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":217,"outFeatures":183}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":183,"outFeatures":183}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":26,"outFeatures":183}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":183,"outFeatures":183}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":366,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":111813}} {"id":"gen-grow-919","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-919\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":1348}},{"type":"update_params","name":"fc2","params":{"inFeatures":1348,"outFeatures":1348}},{"type":"update_params","name":"head","params":{"inFeatures":1348,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":1973472}} {"id":"gen-mlp-920","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 405-feature input with 6 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-920\nComponents:\n - input (input) params={\"shape\":[1,405]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,405]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":405,"outFeatures":153}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":153,"outFeatures":595}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":595,"outFeatures":524}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":524,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":467924}} {"id":"gen-ae-921","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 728-dim input: encode down to a 127-dim bottleneck and decode back to 728. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-921\nComponents:\n - input (input) params={\"shape\":[1,728]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,728]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":728,"outFeatures":686}},{"componentType":"linear","name":"enc2","params":{"inFeatures":686,"outFeatures":127}},{"componentType":"linear","name":"dec1","params":{"inFeatures":127,"outFeatures":686}},{"componentType":"linear","name":"dec2","params":{"inFeatures":686,"outFeatures":728}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1173060}} {"id":"gen-cnn-922","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 95 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-922\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":10404}} {"id":"gen-txf-923","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 200-token sequences with 53 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-923\nComponents:\n - input (input) params={\"shape\":[1,200]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,200]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35697,"embeddingDim":56}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":56,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":56,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":56,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2039632}} {"id":"gen-gqa-924","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 375-token sequences with 14 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-924\nComponents:\n - input (input) params={\"shape\":[1,375]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,375]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20572,"embeddingDim":328}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":328,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[328]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":328,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[328]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":328,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[328]}},{"componentType":"linear","name":"head","params":{"inFeatures":328,"outFeatures":14}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6752208}} {"id":"gen-fix-925","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (162) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-925\nComponents:\n - input (input) params={\"shape\":[1,470]}\n - embed (embedding) params={\"numEmbeddings\":31222,\"embeddingDim\":162}\n - attn (multiHeadAttention) params={\"embedDim\":162,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":162,\"outFeatures\":65}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5173470}} {"id":"gen-trim-926","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3735-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-926\nComponents:\n - input (input) params={\"shape\":[1,162]}\n - fc1 (linear) params={\"inFeatures\":162,\"outFeatures\":3735}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3735,\"outFeatures\":3735}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3735,\"outFeatures\":3735}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3735,\"outFeatures\":3735}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3735,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":162,"outFeatures":70}},{"type":"update_params","name":"fc2","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"fc3","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"fc4","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"head","params":{"inFeatures":70,"outFeatures":35}}],"grade":{"pass":true,"score":88,"params":28490}} {"id":"gen-norm-927","family":"norm","seed":20260716,"spec":"This 23-feature, 64-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-927\nComponents:\n - input (input) params={\"shape\":[1,23]}\n - fc1 (linear) params={\"inFeatures\":23,\"outFeatures\":490}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":490,\"outFeatures\":146}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":146,\"outFeatures\":373}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":373,\"outFeatures\":348}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":348,\"outFeatures\":64}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":490}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":146}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":373}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":348}}],"grade":{"pass":true,"score":100,"params":289344}} {"id":"gen-tower-928","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 75-feature user input and a 241-feature item input, each through its own 2-layer MLP tower ending at width 136, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-928\nComponents:\n - user_input (input) params={\"shape\":[1,75]}\n - item_input (input) params={\"shape\":[1,241]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,75]}},{"componentType":"input","name":"item_input","params":{"shape":[1,241]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":75,"outFeatures":136}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":136,"outFeatures":136}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":241,"outFeatures":136}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":136,"outFeatures":136}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":80240}} {"id":"gen-grow-929","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (90-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-929\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":90,"outFeatures":690}},{"type":"update_params","name":"fc2","params":{"inFeatures":690,"outFeatures":690}},{"type":"update_params","name":"head","params":{"inFeatures":690,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":568560}} {"id":"gen-mlp-930","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 288-feature input with 46 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-930\nComponents:\n - input (input) params={\"shape\":[1,288]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,288]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":288,"outFeatures":572}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":572,"outFeatures":126}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":126,"outFeatures":89}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":89,"outFeatures":84}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":259362}} {"id":"gen-ae-931","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1573-dim input: encode down to a 170-dim bottleneck and decode back to 1573. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-931\nComponents:\n - input (input) params={\"shape\":[1,1573]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1573]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1573,"outFeatures":243}},{"componentType":"linear","name":"enc2","params":{"inFeatures":243,"outFeatures":170}},{"componentType":"linear","name":"dec1","params":{"inFeatures":170,"outFeatures":243}},{"componentType":"linear","name":"dec2","params":{"inFeatures":243,"outFeatures":1573}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":847098}} {"id":"gen-cnn-932","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 69 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-932\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":13,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":27906}} {"id":"gen-txf-933","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 35-token sequences with 77 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-933\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,35]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44795,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":104,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4796480}} {"id":"gen-gqa-934","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 456-token sequences with 34 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-934\nComponents:\n - input (input) params={\"shape\":[1,456]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,456]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39599,"embeddingDim":144}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":144,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":144,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[144]}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5707152}} {"id":"gen-fix-935","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (94) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-935\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - embed (embedding) params={\"numEmbeddings\":26183,\"embeddingDim\":94}\n - attn (multiHeadAttention) params={\"embedDim\":94,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":94,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2499460}} {"id":"gen-trim-936","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1738-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-936\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - fc1 (linear) params={\"inFeatures\":94,\"outFeatures\":1738}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1738,\"outFeatures\":1738}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1738,\"outFeatures\":1738}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1738,\"outFeatures\":1738}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1738,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":94,"outFeatures":92}},{"type":"update_params","name":"fc2","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"fc3","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"fc4","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"head","params":{"inFeatures":92,"outFeatures":30}}],"grade":{"pass":true,"score":88,"params":36800}} {"id":"gen-norm-937","family":"norm","seed":20260716,"spec":"This 145-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-937\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":66}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":66,\"outFeatures\":509}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":509,\"outFeatures\":119}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":119,\"outFeatures\":280}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":280,\"outFeatures\":59}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":59,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":66}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":509}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":119}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":280}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":59}}],"grade":{"pass":true,"score":100,"params":158708}} {"id":"gen-tower-938","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 57-feature user input and a 238-feature item input, each through its own 2-layer MLP tower ending at width 297, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-938\nComponents:\n - user_input (input) params={\"shape\":[1,57]}\n - item_input (input) params={\"shape\":[1,238]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,57]}},{"componentType":"input","name":"item_input","params":{"shape":[1,238]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":57,"outFeatures":297}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":238,"outFeatures":297}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":297,"outFeatures":297}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":594,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":264627}} {"id":"gen-grow-939","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (116-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-939\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":1305}},{"type":"update_params","name":"fc2","params":{"inFeatures":1305,"outFeatures":1305}},{"type":"update_params","name":"head","params":{"inFeatures":1305,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":1918350}} {"id":"gen-mlp-940","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 505-feature input with 160 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-940\nComponents:\n - input (input) params={\"shape\":[1,505]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,505]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":505,"outFeatures":122}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":122,"outFeatures":913}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":913,"outFeatures":913}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":913,"outFeatures":673}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":673,"outFeatures":160}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1728694}} {"id":"gen-ae-941","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1050-dim input: encode down to a 142-dim bottleneck and decode back to 1050. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-941\nComponents:\n - input (input) params={\"shape\":[1,1050]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1050]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1050,"outFeatures":270}},{"componentType":"linear","name":"enc2","params":{"inFeatures":270,"outFeatures":142}},{"componentType":"linear","name":"dec1","params":{"inFeatures":142,"outFeatures":270}},{"componentType":"linear","name":"dec2","params":{"inFeatures":270,"outFeatures":1050}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":643680}} {"id":"gen-cnn-942","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 6 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-942\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":35,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":36,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":52026}} {"id":"gen-txf-943","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 408-token sequences with 98 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-943\nComponents:\n - input (input) params={\"shape\":[1,408]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,408]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13806,"embeddingDim":360}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":360,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":360,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":6042240}} {"id":"gen-gqa-944","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 227-token sequences with 94 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-944\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34234,"embeddingDim":72}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":72,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[72]}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2471616}} {"id":"gen-fix-945","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (480) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-945\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - embed (embedding) params={\"numEmbeddings\":29693,\"embeddingDim\":480}\n - attn (multiHeadAttention) params={\"embedDim\":480,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":480,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":15196800}} {"id":"gen-trim-946","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2672-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-946\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":2672}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2672,\"outFeatures\":2672}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2672,\"outFeatures\":2672}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2672,\"outFeatures\":2672}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2672,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":190,"outFeatures":321}},{"type":"update_params","name":"fc2","params":{"inFeatures":321,"outFeatures":321}},{"type":"update_params","name":"fc3","params":{"inFeatures":321,"outFeatures":321}},{"type":"update_params","name":"fc4","params":{"inFeatures":321,"outFeatures":321}},{"type":"update_params","name":"head","params":{"inFeatures":321,"outFeatures":50}}],"grade":{"pass":true,"score":88,"params":386163}} {"id":"gen-norm-947","family":"norm","seed":20260716,"spec":"This 118-feature, 82-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-947\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":471}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":471,\"outFeatures\":147}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":147,\"outFeatures\":116}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":116,\"outFeatures\":82}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":471}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":147}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":116}}],"grade":{"pass":true,"score":100,"params":151379}} {"id":"gen-tower-948","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 158-feature user input and a 185-feature item input, each through its own 2-layer MLP tower ending at width 390, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-948\nComponents:\n - user_input (input) params={\"shape\":[1,158]}\n - item_input (input) params={\"shape\":[1,185]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,158]}},{"componentType":"input","name":"item_input","params":{"shape":[1,185]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":158,"outFeatures":390}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":390,"outFeatures":390}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":185,"outFeatures":390}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":390,"outFeatures":390}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":780,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":438750}} {"id":"gen-grow-949","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (127-feature input, 47 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-949\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":127,"outFeatures":1109}},{"type":"update_params","name":"fc2","params":{"inFeatures":1109,"outFeatures":1109}},{"type":"update_params","name":"head","params":{"inFeatures":1109,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":1422847}} {"id":"gen-mlp-950","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 460-feature input with 197 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-950\nComponents:\n - input (input) params={\"shape\":[1,460]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,460]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":460,"outFeatures":856}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":856,"outFeatures":239}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":239,"outFeatures":812}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":812,"outFeatures":488}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":488,"outFeatures":731}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":731,"outFeatures":197}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1689403}} {"id":"gen-ae-951","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 806-dim input: encode down to a 60-dim bottleneck and decode back to 806. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-951\nComponents:\n - input (input) params={\"shape\":[1,806]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,806]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":806,"outFeatures":1016}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1016,"outFeatures":60}},{"componentType":"linear","name":"dec1","params":{"inFeatures":60,"outFeatures":1016}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1016,"outFeatures":806}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1759712}} {"id":"gen-cnn-952","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x57x57 image with 65 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-952\nComponents:\n - input (input) params={\"shape\":[1,3,57,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,57,57]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":50,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":53,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":42544}} {"id":"gen-txf-953","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 156-token sequences with 3 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-953\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,156]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34497,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1665216}} {"id":"gen-gqa-954","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 447-token sequences with 53 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-954\nComponents:\n - input (input) params={\"shape\":[1,447]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,447]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34821,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11717664}} {"id":"gen-fix-955","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (158) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-955\nComponents:\n - input (input) params={\"shape\":[1,468]}\n - embed (embedding) params={\"numEmbeddings\":30163,\"embeddingDim\":158}\n - attn (multiHeadAttention) params={\"embedDim\":158,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":158,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4871614}} {"id":"gen-trim-956","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2326-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-956\nComponents:\n - input (input) params={\"shape\":[1,189]}\n - fc1 (linear) params={\"inFeatures\":189,\"outFeatures\":2326}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2326,\"outFeatures\":2326}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2326,\"outFeatures\":2326}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2326,\"outFeatures\":2326}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2326,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":189,"outFeatures":369}},{"type":"update_params","name":"fc2","params":{"inFeatures":369,"outFeatures":369}},{"type":"update_params","name":"fc3","params":{"inFeatures":369,"outFeatures":369}},{"type":"update_params","name":"fc4","params":{"inFeatures":369,"outFeatures":369}},{"type":"update_params","name":"head","params":{"inFeatures":369,"outFeatures":19}}],"grade":{"pass":true,"score":88,"params":485235}} {"id":"gen-norm-957","family":"norm","seed":20260716,"spec":"This 97-feature, 16-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-957\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":427}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":427,\"outFeatures\":172}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":172,\"outFeatures\":489}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":489,\"outFeatures\":392}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":392,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":427}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":172}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":489}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":392}}],"grade":{"pass":true,"score":100,"params":396931}} {"id":"gen-tower-958","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 86-feature user input and a 17-feature item input, each through its own 2-layer MLP tower ending at width 168, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-958\nComponents:\n - user_input (input) params={\"shape\":[1,86]}\n - item_input (input) params={\"shape\":[1,17]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,86]}},{"componentType":"input","name":"item_input","params":{"shape":[1,17]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":86,"outFeatures":168}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":168,"outFeatures":168}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":17,"outFeatures":168}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":168,"outFeatures":168}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":74088}} {"id":"gen-grow-959","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (20-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-959\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":20,"outFeatures":1036}},{"type":"update_params","name":"fc2","params":{"inFeatures":1036,"outFeatures":1036}},{"type":"update_params","name":"head","params":{"inFeatures":1036,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":1141672}} {"id":"gen-mlp-960","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 314-feature input with 40 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-960\nComponents:\n - input (input) params={\"shape\":[1,314]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,314]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":314,"outFeatures":37}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":37,"outFeatures":246}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":246,"outFeatures":20}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":20,"outFeatures":76}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":76,"outFeatures":704}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":704,"outFeatures":692}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":692,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":595512}} {"id":"gen-ae-961","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1087-dim input: encode down to a 195-dim bottleneck and decode back to 1087. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-961\nComponents:\n - input (input) params={\"shape\":[1,1087]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1087]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1087,"outFeatures":685}},{"componentType":"linear","name":"enc2","params":{"inFeatures":685,"outFeatures":195}},{"componentType":"linear","name":"dec1","params":{"inFeatures":195,"outFeatures":685}},{"componentType":"linear","name":"dec2","params":{"inFeatures":685,"outFeatures":1087}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1756340}} {"id":"gen-cnn-962","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 5 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-962\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":41779}} {"id":"gen-txf-963","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 74-token sequences with 33 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-963\nComponents:\n - input (input) params={\"shape\":[1,74]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,74]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17846,"embeddingDim":180}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":180,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":180,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3477420}} {"id":"gen-gqa-964","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 355-token sequences with 37 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-964\nComponents:\n - input (input) params={\"shape\":[1,355]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,355]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39601,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":224,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":8878912}} {"id":"gen-fix-965","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (92) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-965\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - embed (embedding) params={\"numEmbeddings\":32853,\"embeddingDim\":92}\n - attn (multiHeadAttention) params={\"embedDim\":92,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":92,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":3062772}} {"id":"gen-trim-966","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3446-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-966\nComponents:\n - input (input) params={\"shape\":[1,54]}\n - fc1 (linear) params={\"inFeatures\":54,\"outFeatures\":3446}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3446,\"outFeatures\":3446}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3446,\"outFeatures\":3446}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3446,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":54,"outFeatures":99}},{"type":"update_params","name":"fc2","params":{"inFeatures":99,"outFeatures":99}},{"type":"update_params","name":"fc3","params":{"inFeatures":99,"outFeatures":99}},{"type":"update_params","name":"head","params":{"inFeatures":99,"outFeatures":37}}],"grade":{"pass":true,"score":84,"params":28611}} {"id":"gen-norm-967","family":"norm","seed":20260716,"spec":"This 53-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-967\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":193}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":193,\"outFeatures\":449}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":449,\"outFeatures\":224}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":224,\"outFeatures\":504}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":504,\"outFeatures\":360}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":360,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":193}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":449}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":224}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":504}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":360}}],"grade":{"pass":true,"score":100,"params":518438}} {"id":"gen-tower-968","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 242-feature user input and a 147-feature item input, each through its own 2-layer MLP tower ending at width 411, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-968\nComponents:\n - user_input (input) params={\"shape\":[1,242]}\n - item_input (input) params={\"shape\":[1,147]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,242]}},{"componentType":"input","name":"item_input","params":{"shape":[1,147]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":242,"outFeatures":411}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":411,"outFeatures":411}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":147,"outFeatures":411}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":411,"outFeatures":411}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":822,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":498543}} {"id":"gen-grow-969","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (30-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-969\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":30,"outFeatures":957}},{"type":"update_params","name":"fc2","params":{"inFeatures":957,"outFeatures":957}},{"type":"update_params","name":"head","params":{"inFeatures":957,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":957957}} {"id":"gen-mlp-970","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 487-feature input with 73 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-970\nComponents:\n - input (input) params={\"shape\":[1,487]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,487]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":487,"outFeatures":398}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":398,"outFeatures":581}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":581,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":467477}} {"id":"gen-ae-971","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 872-dim input: encode down to a 175-dim bottleneck and decode back to 872. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-971\nComponents:\n - input (input) params={\"shape\":[1,872]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,872]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":872,"outFeatures":804}},{"componentType":"linear","name":"enc2","params":{"inFeatures":804,"outFeatures":175}},{"componentType":"linear","name":"dec1","params":{"inFeatures":175,"outFeatures":804}},{"componentType":"linear","name":"dec2","params":{"inFeatures":804,"outFeatures":872}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1683576}} {"id":"gen-cnn-972","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x26x26 image with 60 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-972\nComponents:\n - input (input) params={\"shape\":[1,3,26,26]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,26,26]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":50904}} {"id":"gen-txf-973","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 418-token sequences with 6 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-973\nComponents:\n - input (input) params={\"shape\":[1,418]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,418]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41776,"embeddingDim":488}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":488,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":488,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":22294768}} {"id":"gen-gqa-974","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 175-token sequences with 4 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-974\nComponents:\n - input (input) params={\"shape\":[1,175]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,175]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35575,"embeddingDim":240}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":240,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":240,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":240,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[240]}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":8538960}} {"id":"gen-trim-976","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3644-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-976\nComponents:\n - input (input) params={\"shape\":[1,149]}\n - fc1 (linear) params={\"inFeatures\":149,\"outFeatures\":3644}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3644,\"outFeatures\":3644}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3644,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":149,"outFeatures":371}},{"type":"update_params","name":"fc2","params":{"inFeatures":371,"outFeatures":371}},{"type":"update_params","name":"head","params":{"inFeatures":371,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":201824}} {"id":"gen-norm-977","family":"norm","seed":20260716,"spec":"This 68-feature, 85-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-977\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":296}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":296,\"outFeatures\":229}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":229,\"outFeatures\":402}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":402,\"outFeatures\":246}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":246,\"outFeatures\":398}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":398,\"outFeatures\":85}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":296}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":229}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":402}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":246}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":398}}],"grade":{"pass":true,"score":100,"params":410600}} {"id":"gen-tower-978","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 39-feature user input and a 119-feature item input, each through its own 2-layer MLP tower ending at width 458, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-978\nComponents:\n - user_input (input) params={\"shape\":[1,39]}\n - item_input (input) params={\"shape\":[1,119]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,39]}},{"componentType":"input","name":"item_input","params":{"shape":[1,119]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":39,"outFeatures":458}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":458,"outFeatures":458}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":119,"outFeatures":458}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":458,"outFeatures":458}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":916,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":492808}} {"id":"gen-grow-979","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (71-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-979\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - fc1 (linear) params={\"inFeatures\":71,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":71,"outFeatures":751}},{"type":"update_params","name":"fc2","params":{"inFeatures":751,"outFeatures":751}},{"type":"update_params","name":"head","params":{"inFeatures":751,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":632342}} {"id":"gen-mlp-980","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 438-feature input with 118 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-980\nComponents:\n - input (input) params={\"shape\":[1,438]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,438]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":438,"outFeatures":886}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":886,"outFeatures":417}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":417,"outFeatures":277}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":277,"outFeatures":266}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":266,"outFeatures":321}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":321,"outFeatures":658}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":658,"outFeatures":118}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1320969}} {"id":"gen-ae-981","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1010-dim input: encode down to a 47-dim bottleneck and decode back to 1010. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-981\nComponents:\n - input (input) params={\"shape\":[1,1010]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1010]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1010,"outFeatures":319}},{"componentType":"linear","name":"enc2","params":{"inFeatures":319,"outFeatures":47}},{"componentType":"linear","name":"dec1","params":{"inFeatures":47,"outFeatures":319}},{"componentType":"linear","name":"dec2","params":{"inFeatures":319,"outFeatures":1010}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":674366}} {"id":"gen-cnn-982","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 47 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-982\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":41,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":8,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":31314}} {"id":"gen-txf-983","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 174-token sequences with 49 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-983\nComponents:\n - input (input) params={\"shape\":[1,174]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,174]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14994,"embeddingDim":256}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":256,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":256,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":256,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4637440}} {"id":"gen-gqa-984","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 281-token sequences with 33 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-984\nComponents:\n - input (input) params={\"shape\":[1,281]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,281]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12424,"embeddingDim":40}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[40]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":40,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[40]}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":498280}} {"id":"gen-trim-986","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1597-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-986\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - fc1 (linear) params={\"inFeatures\":134,\"outFeatures\":1597}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1597,\"outFeatures\":1597}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1597,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":134,"outFeatures":271}},{"type":"update_params","name":"fc2","params":{"inFeatures":271,"outFeatures":271}},{"type":"update_params","name":"head","params":{"inFeatures":271,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":121408}} {"id":"gen-norm-987","family":"norm","seed":20260716,"spec":"This 252-feature, 21-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-987\nComponents:\n - input (input) params={\"shape\":[1,252]}\n - fc1 (linear) params={\"inFeatures\":252,\"outFeatures\":128}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":128,\"outFeatures\":120}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":120,\"outFeatures\":511}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":511,\"outFeatures\":412}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":412,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":128}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":120}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":511}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":412}}],"grade":{"pass":true,"score":100,"params":328120}} {"id":"gen-tower-988","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 167-feature user input and a 201-feature item input, each through its own 2-layer MLP tower ending at width 133, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-988\nComponents:\n - user_input (input) params={\"shape\":[1,167]}\n - item_input (input) params={\"shape\":[1,201]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,167]}},{"componentType":"input","name":"item_input","params":{"shape":[1,201]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":167,"outFeatures":133}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":133,"outFeatures":133}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":201,"outFeatures":133}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":133,"outFeatures":133}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":266,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":84588}} {"id":"gen-grow-989","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-989\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":737}},{"type":"update_params","name":"fc2","params":{"inFeatures":737,"outFeatures":737}},{"type":"update_params","name":"head","params":{"inFeatures":737,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":580019}} {"id":"gen-mlp-990","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 445-feature input with 60 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-990\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":445,"outFeatures":511}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":511,"outFeatures":825}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":825,"outFeatures":1017}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":1017,"outFeatures":23}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1512766}} {"id":"gen-ae-991","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1284-dim input: encode down to a 233-dim bottleneck and decode back to 1284. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-991\nComponents:\n - input (input) params={\"shape\":[1,1284]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1284]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1284,"outFeatures":650}},{"componentType":"linear","name":"enc2","params":{"inFeatures":650,"outFeatures":233}},{"componentType":"linear","name":"dec1","params":{"inFeatures":233,"outFeatures":650}},{"componentType":"linear","name":"dec2","params":{"inFeatures":650,"outFeatures":1284}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1972100}} {"id":"gen-cnn-992","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 56 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-992\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":29,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":33438}} {"id":"gen-txf-993","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 464-token sequences with 28 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-993\nComponents:\n - input (input) params={\"shape\":[1,464]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,464]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21847,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":200,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4855000}} {"id":"gen-gqa-994","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 295-token sequences with 60 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-994\nComponents:\n - input (input) params={\"shape\":[1,295]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,295]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24955,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7204320}} {"id":"gen-trim-996","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2810-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-996\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":2810}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2810,\"outFeatures\":2810}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2810,\"outFeatures\":2810}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2810,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":217,"outFeatures":303}},{"type":"update_params","name":"fc2","params":{"inFeatures":303,"outFeatures":303}},{"type":"update_params","name":"fc3","params":{"inFeatures":303,"outFeatures":303}},{"type":"update_params","name":"head","params":{"inFeatures":303,"outFeatures":32}}],"grade":{"pass":true,"score":84,"params":259065}} {"id":"gen-norm-997","family":"norm","seed":20260716,"spec":"This 120-feature, 79-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-997\nComponents:\n - input (input) params={\"shape\":[1,120]}\n - fc1 (linear) params={\"inFeatures\":120,\"outFeatures\":177}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":177,\"outFeatures\":44}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":44,\"outFeatures\":393}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":393,\"outFeatures\":79}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":177}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":44}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":393}}],"grade":{"pass":true,"score":100,"params":77367}} {"id":"gen-tower-998","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 219-feature user input and a 189-feature item input, each through its own 2-layer MLP tower ending at width 461, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-998\nComponents:\n - user_input (input) params={\"shape\":[1,219]}\n - item_input (input) params={\"shape\":[1,189]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,219]}},{"componentType":"input","name":"item_input","params":{"shape":[1,189]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":219,"outFeatures":461}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":461,"outFeatures":461}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":189,"outFeatures":461}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":461,"outFeatures":461}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":922,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":614052}} {"id":"gen-grow-999","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (63-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-999\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - fc1 (linear) params={\"inFeatures\":63,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":63,"outFeatures":755}},{"type":"update_params","name":"fc2","params":{"inFeatures":755,"outFeatures":755}},{"type":"update_params","name":"head","params":{"inFeatures":755,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":625895}} {"id":"gen-mlp-1000","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 40-feature input with 16 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1000\nComponents:\n - input (input) params={\"shape\":[1,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,40]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":40,"outFeatures":84}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":84,"outFeatures":788}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":788,"outFeatures":265}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":265,"outFeatures":744}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":744,"outFeatures":506}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":506,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":860092}} {"id":"gen-ae-1001","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 192-dim input: encode down to a 10-dim bottleneck and decode back to 192. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1001\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,192]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":192,"outFeatures":198}},{"componentType":"linear","name":"enc2","params":{"inFeatures":198,"outFeatures":10}},{"componentType":"linear","name":"dec1","params":{"inFeatures":10,"outFeatures":198}},{"componentType":"linear","name":"dec2","params":{"inFeatures":198,"outFeatures":192}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":79992}} {"id":"gen-cnn-1002","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 24 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1002\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":11,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":41,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":22053}} {"id":"gen-txf-1003","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 484-token sequences with 78 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1003\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32207,"embeddingDim":82}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":82,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":82,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":82,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2701162}} {"id":"gen-gqa-1004","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 478-token sequences with 27 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1004\nComponents:\n - input (input) params={\"shape\":[1,478]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,478]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34660,"embeddingDim":480}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":480,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[480]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":480,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[480]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":480,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[480]}},{"componentType":"linear","name":"head","params":{"inFeatures":480,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":16649760}} {"id":"gen-trim-1006","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3701-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1006\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":3701}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3701,\"outFeatures\":3701}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3701,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":41,"outFeatures":338}},{"type":"update_params","name":"fc2","params":{"inFeatures":338,"outFeatures":338}},{"type":"update_params","name":"head","params":{"inFeatures":338,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":137228}} {"id":"gen-norm-1007","family":"norm","seed":20260716,"spec":"This 124-feature, 44-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1007\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - fc1 (linear) params={\"inFeatures\":124,\"outFeatures\":88}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":88,\"outFeatures\":244}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":244,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":88}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":244}}],"grade":{"pass":true,"score":94,"params":43120}} {"id":"gen-tower-1008","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 61-feature user input and a 28-feature item input, each through its own 2-layer MLP tower ending at width 55, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1008\nComponents:\n - user_input (input) params={\"shape\":[1,61]}\n - item_input (input) params={\"shape\":[1,28]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,61]}},{"componentType":"input","name":"item_input","params":{"shape":[1,28]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":61,"outFeatures":55}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":55,"outFeatures":55}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":28,"outFeatures":55}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":55,"outFeatures":55}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":11055}} {"id":"gen-grow-1009","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (119-feature input, 34 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1009\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - fc1 (linear) params={\"inFeatures\":119,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":119,"outFeatures":1123}},{"type":"update_params","name":"fc2","params":{"inFeatures":1123,"outFeatures":1123}},{"type":"update_params","name":"head","params":{"inFeatures":1123,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":1432948}} {"id":"gen-mlp-1010","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 477-feature input with 103 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1010\nComponents:\n - input (input) params={\"shape\":[1,477]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,477]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":477,"outFeatures":585}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":585,"outFeatures":484}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":484,"outFeatures":21}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":21,"outFeatures":103}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":574512}} {"id":"gen-ae-1011","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1989-dim input: encode down to a 25-dim bottleneck and decode back to 1989. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1011\nComponents:\n - input (input) params={\"shape\":[1,1989]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1989]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1989,"outFeatures":534}},{"componentType":"linear","name":"enc2","params":{"inFeatures":534,"outFeatures":25}},{"componentType":"linear","name":"dec1","params":{"inFeatures":25,"outFeatures":534}},{"componentType":"linear","name":"dec2","params":{"inFeatures":534,"outFeatures":1989}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2150952}} {"id":"gen-cnn-1012","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 59 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1012\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":60,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":50,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":67843}} {"id":"gen-txf-1013","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 221-token sequences with 86 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1013\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,221]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43254,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":116,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5135088}} {"id":"gen-gqa-1014","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 357-token sequences with 23 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1014\nComponents:\n - input (input) params={\"shape\":[1,357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,357]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22842,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3658400}} {"id":"gen-trim-1016","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2095-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1016\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":2095}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2095,\"outFeatures\":2095}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2095,\"outFeatures\":2095}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2095,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":80,"outFeatures":276}},{"type":"update_params","name":"fc2","params":{"inFeatures":276,"outFeatures":276}},{"type":"update_params","name":"fc3","params":{"inFeatures":276,"outFeatures":276}},{"type":"update_params","name":"head","params":{"inFeatures":276,"outFeatures":11}}],"grade":{"pass":true,"score":84,"params":177468}} {"id":"gen-norm-1017","family":"norm","seed":20260716,"spec":"This 181-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1017\nComponents:\n - input (input) params={\"shape\":[1,181]}\n - fc1 (linear) params={\"inFeatures\":181,\"outFeatures\":150}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":150,\"outFeatures\":440}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":440,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":150}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":440}}],"grade":{"pass":true,"score":94,"params":127470}} {"id":"gen-tower-1018","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 238-feature user input and a 250-feature item input, each through its own 2-layer MLP tower ending at width 362, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1018\nComponents:\n - user_input (input) params={\"shape\":[1,238]}\n - item_input (input) params={\"shape\":[1,250]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,238]}},{"componentType":"input","name":"item_input","params":{"shape":[1,250]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":238,"outFeatures":362}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":362,"outFeatures":362}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":250,"outFeatures":362}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":362,"outFeatures":362}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":724,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":439468}} {"id":"gen-grow-1019","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (49-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1019\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":982}},{"type":"update_params","name":"fc2","params":{"inFeatures":982,"outFeatures":982}},{"type":"update_params","name":"head","params":{"inFeatures":982,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1054668}} {"id":"gen-mlp-1020","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 127-feature input with 155 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1020\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,127]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":127,"outFeatures":242}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":242,"outFeatures":229}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":229,"outFeatures":208}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":208,"outFeatures":892}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":892,"outFeatures":895}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":895,"outFeatures":643}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":643,"outFeatures":155}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1792810}} {"id":"gen-ae-1021","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 697-dim input: encode down to a 174-dim bottleneck and decode back to 697. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1021\nComponents:\n - input (input) params={\"shape\":[1,697]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,697]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":697,"outFeatures":837}},{"componentType":"linear","name":"enc2","params":{"inFeatures":837,"outFeatures":174}},{"componentType":"linear","name":"dec1","params":{"inFeatures":174,"outFeatures":837}},{"componentType":"linear","name":"dec2","params":{"inFeatures":837,"outFeatures":697}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1458054}} {"id":"gen-cnn-1022","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 69 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1022\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":21993}} {"id":"gen-txf-1023","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 207-token sequences with 59 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1023\nComponents:\n - input (input) params={\"shape\":[1,207]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,207]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10173,"embeddingDim":192}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":192,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2112000}} {"id":"gen-gqa-1024","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 236-token sequences with 38 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1024\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,236]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8543,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":1372960}} {"id":"gen-trim-1026","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3796-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1026\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - fc1 (linear) params={\"inFeatures\":213,\"outFeatures\":3796}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3796,\"outFeatures\":3796}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3796,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":213,"outFeatures":129}},{"type":"update_params","name":"fc2","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"head","params":{"inFeatures":129,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":46182}} {"id":"gen-norm-1027","family":"norm","seed":20260716,"spec":"This 76-feature, 94-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1027\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":491}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":491,\"outFeatures\":194}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":194,\"outFeatures\":94}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":491}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":194}}],"grade":{"pass":true,"score":94,"params":150806}} {"id":"gen-tower-1028","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 63-feature user input and a 149-feature item input, each through its own 2-layer MLP tower ending at width 40, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1028\nComponents:\n - user_input (input) params={\"shape\":[1,63]}\n - item_input (input) params={\"shape\":[1,149]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,63]}},{"componentType":"input","name":"item_input","params":{"shape":[1,149]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":63,"outFeatures":40}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":40,"outFeatures":40}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":149,"outFeatures":40}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":40,"outFeatures":40}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":11760}} {"id":"gen-grow-1029","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (117-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1029\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":670}},{"type":"update_params","name":"fc2","params":{"inFeatures":670,"outFeatures":670}},{"type":"update_params","name":"head","params":{"inFeatures":670,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":544710}} {"id":"gen-mlp-1030","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 78-feature input with 146 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1030\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,78]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":78,"outFeatures":420}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":420,"outFeatures":182}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":182,"outFeatures":762}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":762,"outFeatures":614}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":614,"outFeatures":316}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":316,"outFeatures":146}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":955912}} {"id":"gen-ae-1031","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1265-dim input: encode down to a 238-dim bottleneck and decode back to 1265. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1031\nComponents:\n - input (input) params={\"shape\":[1,1265]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1265]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1265,"outFeatures":242}},{"componentType":"linear","name":"enc2","params":{"inFeatures":242,"outFeatures":238}},{"componentType":"linear","name":"dec1","params":{"inFeatures":238,"outFeatures":242}},{"componentType":"linear","name":"dec2","params":{"inFeatures":242,"outFeatures":1265}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":727452}} {"id":"gen-cnn-1032","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 12 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1032\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":32,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":63,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":46,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":63408}} {"id":"gen-txf-1033","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 233-token sequences with 45 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1033\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,233]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24573,"embeddingDim":96}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":96,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":96,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":96,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2473920}} {"id":"gen-gqa-1034","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 326-token sequences with 5 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1034\nComponents:\n - input (input) params={\"shape\":[1,326]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,326]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23081,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":9234400}} {"id":"gen-trim-1036","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2397-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1036\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - fc1 (linear) params={\"inFeatures\":208,\"outFeatures\":2397}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2397,\"outFeatures\":2397}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2397,\"outFeatures\":2397}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2397,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":208,"outFeatures":265}},{"type":"update_params","name":"fc2","params":{"inFeatures":265,"outFeatures":265}},{"type":"update_params","name":"fc3","params":{"inFeatures":265,"outFeatures":265}},{"type":"update_params","name":"head","params":{"inFeatures":265,"outFeatures":13}}],"grade":{"pass":true,"score":84,"params":199015}} {"id":"gen-norm-1037","family":"norm","seed":20260716,"spec":"This 208-feature, 11-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1037\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - fc1 (linear) params={\"inFeatures\":208,\"outFeatures\":241}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":241,\"outFeatures\":423}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":423,\"outFeatures\":177}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":177,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":241}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":423}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":177}}],"grade":{"pass":true,"score":100,"params":228889}} {"id":"gen-tower-1038","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 256-feature user input and a 96-feature item input, each through its own 2-layer MLP tower ending at width 298, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1038\nComponents:\n - user_input (input) params={\"shape\":[1,256]}\n - item_input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,256]}},{"componentType":"input","name":"item_input","params":{"shape":[1,96]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":256,"outFeatures":298}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":298,"outFeatures":298}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":96,"outFeatures":298}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":298,"outFeatures":298}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":596,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":283100}} {"id":"gen-grow-1039","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (89-feature input, 17 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1039\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - fc1 (linear) params={\"inFeatures\":89,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":89,"outFeatures":1235}},{"type":"update_params","name":"fc2","params":{"inFeatures":1235,"outFeatures":1235}},{"type":"update_params","name":"head","params":{"inFeatures":1235,"outFeatures":17}}],"grade":{"pass":true,"score":80,"params":1656135}} {"id":"gen-mlp-1040","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 167-feature input with 62 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1040\nComponents:\n - input (input) params={\"shape\":[1,167]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,167]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":167,"outFeatures":262}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":262,"outFeatures":341}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":341,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":154238}} {"id":"gen-ae-1041","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1791-dim input: encode down to a 204-dim bottleneck and decode back to 1791. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1041\nComponents:\n - input (input) params={\"shape\":[1,1791]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1791]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1791,"outFeatures":456}},{"componentType":"linear","name":"enc2","params":{"inFeatures":456,"outFeatures":204}},{"componentType":"linear","name":"dec1","params":{"inFeatures":204,"outFeatures":456}},{"componentType":"linear","name":"dec2","params":{"inFeatures":456,"outFeatures":1791}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1819440}} {"id":"gen-cnn-1042","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 5 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1042\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":50,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":32,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":22782}} {"id":"gen-txf-1043","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 263-token sequences with 47 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1043\nComponents:\n - input (input) params={\"shape\":[1,263]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,263]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37552,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4415308}} {"id":"gen-gqa-1044","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 113-token sequences with 97 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1044\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,113]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49630,"embeddingDim":280}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":280,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[280]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":280,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[280]}},{"componentType":"linear","name":"head","params":{"inFeatures":280,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":13923560}} {"id":"gen-trim-1046","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2184-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1046\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - fc1 (linear) params={\"inFeatures\":199,\"outFeatures\":2184}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2184,\"outFeatures\":2184}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2184,\"outFeatures\":2184}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2184,\"outFeatures\":2184}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2184,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":199,"outFeatures":301}},{"type":"update_params","name":"fc2","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"fc3","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"fc4","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"head","params":{"inFeatures":301,"outFeatures":7}}],"grade":{"pass":true,"score":88,"params":333809}} {"id":"gen-norm-1047","family":"norm","seed":20260716,"spec":"This 41-feature, 3-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1047\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":305}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":305,\"outFeatures\":40}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":40,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":305}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":40}}],"grade":{"pass":true,"score":94,"params":24825}} {"id":"gen-tower-1048","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 246-feature user input and a 229-feature item input, each through its own 2-layer MLP tower ending at width 186, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1048\nComponents:\n - user_input (input) params={\"shape\":[1,246]}\n - item_input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,246]}},{"componentType":"input","name":"item_input","params":{"shape":[1,229]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":246,"outFeatures":186}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":186,"outFeatures":186}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":229,"outFeatures":186}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":186,"outFeatures":186}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":372,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":157914}} {"id":"gen-grow-1049","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1049\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":1035}},{"type":"update_params","name":"fc2","params":{"inFeatures":1035,"outFeatures":1035}},{"type":"update_params","name":"head","params":{"inFeatures":1035,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":1183005}} {"id":"gen-mlp-1050","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 409-feature input with 5 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1050\nComponents:\n - input (input) params={\"shape\":[1,409]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,409]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":409,"outFeatures":203}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":203,"outFeatures":154}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":154,"outFeatures":439}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":439,"outFeatures":171}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":171,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":257819}} {"id":"gen-ae-1051","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1878-dim input: encode down to a 201-dim bottleneck and decode back to 1878. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1051\nComponents:\n - input (input) params={\"shape\":[1,1878]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1878]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1878,"outFeatures":970}},{"componentType":"linear","name":"enc2","params":{"inFeatures":970,"outFeatures":201}},{"componentType":"linear","name":"dec1","params":{"inFeatures":201,"outFeatures":970}},{"componentType":"linear","name":"dec2","params":{"inFeatures":970,"outFeatures":1878}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4033260}} {"id":"gen-cnn-1052","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 36 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1052\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4545}} {"id":"gen-txf-1053","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 211-token sequences with 60 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1053\nComponents:\n - input (input) params={\"shape\":[1,211]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,211]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9639,"embeddingDim":368}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":368,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":368,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4652624}} {"id":"gen-gqa-1054","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 489-token sequences with 51 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1054\nComponents:\n - input (input) params={\"shape\":[1,489]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,489]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23039,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":14408160}} {"id":"gen-fix-1055","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (378) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1055\nComponents:\n - input (input) params={\"shape\":[1,392]}\n - embed (embedding) params={\"numEmbeddings\":38091,\"embeddingDim\":378}\n - attn (multiHeadAttention) params={\"embedDim\":378,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":378,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":14986566}} {"id":"gen-trim-1056","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3884-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1056\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":3884}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3884,\"outFeatures\":3884}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3884,\"outFeatures\":3884}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3884,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":95,"outFeatures":295}},{"type":"update_params","name":"fc2","params":{"inFeatures":295,"outFeatures":295}},{"type":"update_params","name":"fc3","params":{"inFeatures":295,"outFeatures":295}},{"type":"update_params","name":"head","params":{"inFeatures":295,"outFeatures":3}}],"grade":{"pass":true,"score":84,"params":202960}} {"id":"gen-norm-1057","family":"norm","seed":20260716,"spec":"This 141-feature, 72-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1057\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - fc1 (linear) params={\"inFeatures\":141,\"outFeatures\":313}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":313,\"outFeatures\":188}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":188,\"outFeatures\":212}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":212,\"outFeatures\":87}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":87,\"outFeatures\":377}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":377,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":313}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":188}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":212}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":87}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":377}}],"grade":{"pass":true,"score":100,"params":221220}} {"id":"gen-tower-1058","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 155-feature item input, each through its own 2-layer MLP tower ending at width 334, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1058\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,155]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,155]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":334}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":334,"outFeatures":334}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":155,"outFeatures":334}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":334,"outFeatures":334}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":668,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":355710}} {"id":"gen-grow-1059","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (68-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1059\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":68,"outFeatures":656}},{"type":"update_params","name":"fc2","params":{"inFeatures":656,"outFeatures":656}},{"type":"update_params","name":"head","params":{"inFeatures":656,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":505120}} {"id":"gen-mlp-1060","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 152-feature input with 63 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1060\nComponents:\n - input (input) params={\"shape\":[1,152]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,152]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":152,"outFeatures":453}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":453,"outFeatures":594}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":594,"outFeatures":468}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":468,"outFeatures":617}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":617,"outFeatures":493}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":493,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1239926}} {"id":"gen-ae-1061","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1780-dim input: encode down to a 129-dim bottleneck and decode back to 1780. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1061\nComponents:\n - input (input) params={\"shape\":[1,1780]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1780]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1780,"outFeatures":572}},{"componentType":"linear","name":"enc2","params":{"inFeatures":572,"outFeatures":129}},{"componentType":"linear","name":"dec1","params":{"inFeatures":129,"outFeatures":572}},{"componentType":"linear","name":"dec2","params":{"inFeatures":572,"outFeatures":1780}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2183896}} {"id":"gen-cnn-1062","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 51 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1062\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":27,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7587}} {"id":"gen-txf-1063","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 242-token sequences with 82 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1063\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,242]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30021,"embeddingDim":64}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":64,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1942976}} {"id":"gen-gqa-1064","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 450-token sequences with 75 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1064\nComponents:\n - input (input) params={\"shape\":[1,450]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,450]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37081,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":14862400}} {"id":"gen-fix-1065","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (486) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1065\nComponents:\n - input (input) params={\"shape\":[1,327]}\n - embed (embedding) params={\"numEmbeddings\":37338,\"embeddingDim\":486}\n - attn (multiHeadAttention) params={\"embedDim\":486,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":486,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":19130418}} {"id":"gen-trim-1066","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3191-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1066\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":3191}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3191,\"outFeatures\":3191}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3191,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":159,"outFeatures":103}},{"type":"update_params","name":"fc2","params":{"inFeatures":103,"outFeatures":103}},{"type":"update_params","name":"head","params":{"inFeatures":103,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":30591}} {"id":"gen-norm-1067","family":"norm","seed":20260716,"spec":"This 135-feature, 17-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1067\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - fc1 (linear) params={\"inFeatures\":135,\"outFeatures\":251}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":251,\"outFeatures\":168}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":168,\"outFeatures\":313}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":313,\"outFeatures\":148}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":148,\"outFeatures\":220}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":220,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":251}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":168}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":313}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":148}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":220}}],"grade":{"pass":true,"score":100,"params":211261}} {"id":"gen-tower-1068","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 101-feature user input and a 29-feature item input, each through its own 2-layer MLP tower ending at width 465, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1068\nComponents:\n - user_input (input) params={\"shape\":[1,101]}\n - item_input (input) params={\"shape\":[1,29]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,101]}},{"componentType":"input","name":"item_input","params":{"shape":[1,29]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":101,"outFeatures":465}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":465,"outFeatures":465}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":29,"outFeatures":465}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":465,"outFeatures":465}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":930,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":493830}} {"id":"gen-grow-1069","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (67-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1069\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":67,"outFeatures":790}},{"type":"update_params","name":"fc2","params":{"inFeatures":790,"outFeatures":790}},{"type":"update_params","name":"head","params":{"inFeatures":790,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":680980}} {"id":"gen-mlp-1070","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 325-feature input with 191 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1070\nComponents:\n - input (input) params={\"shape\":[1,325]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,325]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":325,"outFeatures":618}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":618,"outFeatures":341}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":341,"outFeatures":505}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":505,"outFeatures":759}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":759,"outFeatures":989}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":989,"outFeatures":191}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1906638}} {"id":"gen-ae-1071","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1846-dim input: encode down to a 19-dim bottleneck and decode back to 1846. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1071\nComponents:\n - input (input) params={\"shape\":[1,1846]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1846]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1846,"outFeatures":773}},{"componentType":"linear","name":"enc2","params":{"inFeatures":773,"outFeatures":19}},{"componentType":"linear","name":"dec1","params":{"inFeatures":19,"outFeatures":773}},{"componentType":"linear","name":"dec2","params":{"inFeatures":773,"outFeatures":1846}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2883290}} {"id":"gen-cnn-1072","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 93 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1072\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":16,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":47,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":17520}} {"id":"gen-txf-1073","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 166-token sequences with 24 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1073\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,166]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20225,"embeddingDim":112}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":112,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":112,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2368240}} {"id":"gen-gqa-1074","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 260-token sequences with 3 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1074\nComponents:\n - input (input) params={\"shape\":[1,260]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,260]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33901,"embeddingDim":736}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":736,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[736]}},{"componentType":"linear","name":"head","params":{"inFeatures":736,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":24953344}} {"id":"gen-trim-1076","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3398-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1076\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - fc1 (linear) params={\"inFeatures\":227,\"outFeatures\":3398}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3398,\"outFeatures\":3398}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3398,\"outFeatures\":3398}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3398,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":227,"outFeatures":258}},{"type":"update_params","name":"fc2","params":{"inFeatures":258,"outFeatures":258}},{"type":"update_params","name":"fc3","params":{"inFeatures":258,"outFeatures":258}},{"type":"update_params","name":"head","params":{"inFeatures":258,"outFeatures":35}}],"grade":{"pass":true,"score":84,"params":200724}} {"id":"gen-norm-1077","family":"norm","seed":20260716,"spec":"This 19-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1077\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":487}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":487,\"outFeatures\":413}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":413,\"outFeatures\":75}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":75,\"outFeatures\":253}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":253,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":487}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":413}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":75}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":253}}],"grade":{"pass":true,"score":100,"params":267924}} {"id":"gen-tower-1078","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 201-feature user input and a 24-feature item input, each through its own 2-layer MLP tower ending at width 107, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1078\nComponents:\n - user_input (input) params={\"shape\":[1,201]}\n - item_input (input) params={\"shape\":[1,24]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,201]}},{"componentType":"input","name":"item_input","params":{"shape":[1,24]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":201,"outFeatures":107}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":107,"outFeatures":107}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":24,"outFeatures":107}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":107,"outFeatures":107}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":214,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":47187}} {"id":"gen-grow-1079","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (22-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1079\nComponents:\n - input (input) params={\"shape\":[1,22]}\n - fc1 (linear) params={\"inFeatures\":22,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":22,"outFeatures":1400}},{"type":"update_params","name":"fc2","params":{"inFeatures":1400,"outFeatures":1400}},{"type":"update_params","name":"head","params":{"inFeatures":1400,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":2055200}} {"id":"gen-mlp-1080","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 17-feature input with 69 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1080\nComponents:\n - input (input) params={\"shape\":[1,17]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,17]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":17,"outFeatures":993}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":993,"outFeatures":601}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":601,"outFeatures":643}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":643,"outFeatures":999}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":999,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1711405}} {"id":"gen-ae-1081","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1444-dim input: encode down to a 147-dim bottleneck and decode back to 1444. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1081\nComponents:\n - input (input) params={\"shape\":[1,1444]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1444]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1444,"outFeatures":1021}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1021,"outFeatures":147}},{"componentType":"linear","name":"dec1","params":{"inFeatures":147,"outFeatures":1021}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1021,"outFeatures":1444}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3248822}} {"id":"gen-cnn-1082","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 86 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1082\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":39,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39226}} {"id":"gen-txf-1083","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 219-token sequences with 54 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1083\nComponents:\n - input (input) params={\"shape\":[1,219]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,219]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36607,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2958480}} {"id":"gen-gqa-1084","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 463-token sequences with 69 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1084\nComponents:\n - input (input) params={\"shape\":[1,463]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,463]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21209,"embeddingDim":352}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":352,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[352]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":352,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[352]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":352,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[352]}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7489856}} {"id":"gen-trim-1086","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3370-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1086\nComponents:\n - input (input) params={\"shape\":[1,226]}\n - fc1 (linear) params={\"inFeatures\":226,\"outFeatures\":3370}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3370,\"outFeatures\":3370}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3370,\"outFeatures\":3370}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3370,\"outFeatures\":3370}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3370,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":226,"outFeatures":284}},{"type":"update_params","name":"fc2","params":{"inFeatures":284,"outFeatures":284}},{"type":"update_params","name":"fc3","params":{"inFeatures":284,"outFeatures":284}},{"type":"update_params","name":"fc4","params":{"inFeatures":284,"outFeatures":284}},{"type":"update_params","name":"head","params":{"inFeatures":284,"outFeatures":47}}],"grade":{"pass":true,"score":88,"params":319500}} {"id":"gen-norm-1087","family":"norm","seed":20260716,"spec":"This 129-feature, 49-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1087\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - fc1 (linear) params={\"inFeatures\":129,\"outFeatures\":44}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":44,\"outFeatures\":136}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":136,\"outFeatures\":448}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":448,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":44}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":136}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":448}}],"grade":{"pass":true,"score":100,"params":94540}} {"id":"gen-tower-1088","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 222-feature user input and a 172-feature item input, each through its own 2-layer MLP tower ending at width 365, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1088\nComponents:\n - user_input (input) params={\"shape\":[1,222]}\n - item_input (input) params={\"shape\":[1,172]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,222]}},{"componentType":"input","name":"item_input","params":{"shape":[1,172]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":222,"outFeatures":365}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":365,"outFeatures":365}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":172,"outFeatures":365}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":365,"outFeatures":365}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":730,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":410990}} {"id":"gen-grow-1089","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (16-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1089\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":16,"outFeatures":1176}},{"type":"update_params","name":"fc2","params":{"inFeatures":1176,"outFeatures":1176}},{"type":"update_params","name":"head","params":{"inFeatures":1176,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1406496}} {"id":"gen-mlp-1090","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 182-feature input with 120 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1090\nComponents:\n - input (input) params={\"shape\":[1,182]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,182]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":182,"outFeatures":889}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":889,"outFeatures":213}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":213,"outFeatures":77}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":77,"outFeatures":642}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":642,"outFeatures":609}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":609,"outFeatures":460}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":460,"outFeatures":120}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1143308}} {"id":"gen-ae-1091","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1792-dim input: encode down to a 225-dim bottleneck and decode back to 1792. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1091\nComponents:\n - input (input) params={\"shape\":[1,1792]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1792]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1792,"outFeatures":295}},{"componentType":"linear","name":"enc2","params":{"inFeatures":295,"outFeatures":225}},{"componentType":"linear","name":"dec1","params":{"inFeatures":225,"outFeatures":295}},{"componentType":"linear","name":"dec2","params":{"inFeatures":295,"outFeatures":1792}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1190030}} {"id":"gen-cnn-1092","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 46 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1092\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":36,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":48,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":52386}} {"id":"gen-txf-1093","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 478-token sequences with 30 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1093\nComponents:\n - input (input) params={\"shape\":[1,478]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,478]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19874,"embeddingDim":92}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":92,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":30}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1865024}} {"id":"gen-gqa-1094","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 409-token sequences with 64 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1094\nComponents:\n - input (input) params={\"shape\":[1,409]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,409]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39083,"embeddingDim":144}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":144,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":144,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[144]}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5637168}} {"id":"gen-trim-1096","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3136-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1096\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - fc1 (linear) params={\"inFeatures\":251,\"outFeatures\":3136}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3136,\"outFeatures\":3136}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3136,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":251,"outFeatures":179}},{"type":"update_params","name":"fc2","params":{"inFeatures":179,"outFeatures":179}},{"type":"update_params","name":"head","params":{"inFeatures":179,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":81087}} {"id":"gen-norm-1097","family":"norm","seed":20260716,"spec":"This 159-feature, 40-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1097\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":90}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":90,\"outFeatures\":84}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":84,\"outFeatures\":238}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":238,\"outFeatures\":162}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":162,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":90}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":84}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":238}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":162}}],"grade":{"pass":true,"score":100,"params":86898}} {"id":"gen-tower-1098","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 27-feature user input and a 137-feature item input, each through its own 2-layer MLP tower ending at width 502, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1098\nComponents:\n - user_input (input) params={\"shape\":[1,27]}\n - item_input (input) params={\"shape\":[1,137]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,27]}},{"componentType":"input","name":"item_input","params":{"shape":[1,137]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":27,"outFeatures":502}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":502,"outFeatures":502}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":137,"outFeatures":502}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":502,"outFeatures":502}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1004,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":587340}} {"id":"gen-grow-1099","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (39-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1099\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - fc1 (linear) params={\"inFeatures\":39,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":39,"outFeatures":869}},{"type":"update_params","name":"fc2","params":{"inFeatures":869,"outFeatures":869}},{"type":"update_params","name":"head","params":{"inFeatures":869,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":814253}} {"id":"gen-mlp-1100","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 371-feature input with 196 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1100\nComponents:\n - input (input) params={\"shape\":[1,371]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,371]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":371,"outFeatures":861}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":861,"outFeatures":729}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":729,"outFeatures":634}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":634,"outFeatures":928}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":928,"outFeatures":670}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":670,"outFeatures":767}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":767,"outFeatures":196}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":3283620}} {"id":"gen-ae-1101","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1454-dim input: encode down to a 44-dim bottleneck and decode back to 1454. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1101\nComponents:\n - input (input) params={\"shape\":[1,1454]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1454]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1454,"outFeatures":708}},{"componentType":"linear","name":"enc2","params":{"inFeatures":708,"outFeatures":44}},{"componentType":"linear","name":"dec1","params":{"inFeatures":44,"outFeatures":708}},{"componentType":"linear","name":"dec2","params":{"inFeatures":708,"outFeatures":1454}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2121168}} {"id":"gen-cnn-1102","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 97 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1102\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":28,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":57,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":61876}} {"id":"gen-txf-1103","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 49-token sequences with 30 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1103\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,49]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46962,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":200,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":200,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":30}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":9878400}} {"id":"gen-gqa-1104","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 293-token sequences with 71 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1104\nComponents:\n - input (input) params={\"shape\":[1,293]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,293]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30555,"embeddingDim":528}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":528,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[528]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":528,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[528]}},{"componentType":"linear","name":"head","params":{"inFeatures":528,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":16170528}} {"id":"gen-fix-1105","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (120) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1105\nComponents:\n - input (input) params={\"shape\":[1,239]}\n - embed (embedding) params={\"numEmbeddings\":15382,\"embeddingDim\":120}\n - attn (multiHeadAttention) params={\"embedDim\":120,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":120,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":1908000}} {"id":"gen-trim-1106","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3770-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1106\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - fc1 (linear) params={\"inFeatures\":164,\"outFeatures\":3770}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3770,\"outFeatures\":3770}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3770,\"outFeatures\":3770}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3770,\"outFeatures\":3770}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3770,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":164,"outFeatures":344}},{"type":"update_params","name":"fc2","params":{"inFeatures":344,"outFeatures":344}},{"type":"update_params","name":"fc3","params":{"inFeatures":344,"outFeatures":344}},{"type":"update_params","name":"fc4","params":{"inFeatures":344,"outFeatures":344}},{"type":"update_params","name":"head","params":{"inFeatures":344,"outFeatures":30}}],"grade":{"pass":true,"score":88,"params":421744}} {"id":"gen-norm-1107","family":"norm","seed":20260716,"spec":"This 114-feature, 44-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1107\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":486}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":486,\"outFeatures\":486}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":486,\"outFeatures\":289}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":289,\"outFeatures\":259}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":259,\"outFeatures\":67}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":67,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":486}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":486}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":289}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":259}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":67}}],"grade":{"pass":true,"score":100,"params":527206}} {"id":"gen-tower-1108","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 215-feature user input and a 112-feature item input, each through its own 2-layer MLP tower ending at width 275, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1108\nComponents:\n - user_input (input) params={\"shape\":[1,215]}\n - item_input (input) params={\"shape\":[1,112]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,215]}},{"componentType":"input","name":"item_input","params":{"shape":[1,112]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":215,"outFeatures":275}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":275,"outFeatures":275}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":112,"outFeatures":275}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":275,"outFeatures":275}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":550,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":241725}} {"id":"gen-grow-1109","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (93-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1109\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - fc1 (linear) params={\"inFeatures\":93,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":93,"outFeatures":1387}},{"type":"update_params","name":"fc2","params":{"inFeatures":1387,"outFeatures":1387}},{"type":"update_params","name":"head","params":{"inFeatures":1387,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":2062469}} {"id":"gen-mlp-1110","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 277-feature input with 50 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1110\nComponents:\n - input (input) params={\"shape\":[1,277]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,277]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":277,"outFeatures":186}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":186,"outFeatures":571}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":571,"outFeatures":695}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":695,"outFeatures":595}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":595,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":997848}} {"id":"gen-ae-1111","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1300-dim input: encode down to a 125-dim bottleneck and decode back to 1300. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1111\nComponents:\n - input (input) params={\"shape\":[1,1300]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1300]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1300,"outFeatures":514}},{"componentType":"linear","name":"enc2","params":{"inFeatures":514,"outFeatures":125}},{"componentType":"linear","name":"dec1","params":{"inFeatures":125,"outFeatures":514}},{"componentType":"linear","name":"dec2","params":{"inFeatures":514,"outFeatures":1300}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1464900}} {"id":"gen-cnn-1112","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 89 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1112\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":37,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":11186}} {"id":"gen-txf-1113","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 90-token sequences with 2 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1113\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,90]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27220,"embeddingDim":512}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":512,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":512,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":512,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":512,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":17083392}} {"id":"gen-gqa-1114","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 443-token sequences with 5 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1114\nComponents:\n - input (input) params={\"shape\":[1,443]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,443]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19046,"embeddingDim":352}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":352,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[352]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":352,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[352]}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6705952}} {"id":"gen-trim-1116","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3685-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1116\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":3685}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3685,\"outFeatures\":3685}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3685,\"outFeatures\":3685}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3685,\"outFeatures\":3685}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3685,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":57,"outFeatures":73}},{"type":"update_params","name":"fc2","params":{"inFeatures":73,"outFeatures":73}},{"type":"update_params","name":"fc3","params":{"inFeatures":73,"outFeatures":73}},{"type":"update_params","name":"fc4","params":{"inFeatures":73,"outFeatures":73}},{"type":"update_params","name":"head","params":{"inFeatures":73,"outFeatures":49}}],"grade":{"pass":true,"score":88,"params":23725}} {"id":"gen-norm-1117","family":"norm","seed":20260716,"spec":"This 238-feature, 7-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1117\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - fc1 (linear) params={\"inFeatures\":238,\"outFeatures\":45}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":45,\"outFeatures\":93}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":93,\"outFeatures\":400}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":400,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":45}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":93}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":400}}],"grade":{"pass":true,"score":100,"params":54895}} {"id":"gen-tower-1118","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 84-feature user input and a 56-feature item input, each through its own 2-layer MLP tower ending at width 209, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1118\nComponents:\n - user_input (input) params={\"shape\":[1,84]}\n - item_input (input) params={\"shape\":[1,56]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,84]}},{"componentType":"input","name":"item_input","params":{"shape":[1,56]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":84,"outFeatures":209}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":209,"outFeatures":209}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":56,"outFeatures":209}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":209,"outFeatures":209}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":418,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":117040}} {"id":"gen-grow-1119","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1119\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":1378}},{"type":"update_params","name":"fc2","params":{"inFeatures":1378,"outFeatures":1378}},{"type":"update_params","name":"head","params":{"inFeatures":1378,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1989832}} {"id":"gen-mlp-1120","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 298-feature input with 31 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1120\nComponents:\n - input (input) params={\"shape\":[1,298]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,298]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":298,"outFeatures":385}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":385,"outFeatures":889}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":889,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":484554}} {"id":"gen-ae-1121","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 800-dim input: encode down to a 87-dim bottleneck and decode back to 800. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1121\nComponents:\n - input (input) params={\"shape\":[1,800]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,800]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":800,"outFeatures":306}},{"componentType":"linear","name":"enc2","params":{"inFeatures":306,"outFeatures":87}},{"componentType":"linear","name":"dec1","params":{"inFeatures":87,"outFeatures":306}},{"componentType":"linear","name":"dec2","params":{"inFeatures":306,"outFeatures":800}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":542844}} {"id":"gen-cnn-1122","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 88 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1122\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":17530}} {"id":"gen-txf-1123","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 227-token sequences with 96 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1123\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46289,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":88,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4174808}} {"id":"gen-gqa-1124","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 441-token sequences with 96 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1124\nComponents:\n - input (input) params={\"shape\":[1,441]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,441]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28311,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":256,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7272192}} {"id":"gen-trim-1126","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3255-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1126\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":3255}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3255,\"outFeatures\":3255}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3255,\"outFeatures\":3255}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3255,\"outFeatures\":3255}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3255,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":310}},{"type":"update_params","name":"fc2","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"fc3","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"fc4","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"head","params":{"inFeatures":310,"outFeatures":46}}],"grade":{"pass":true,"score":88,"params":312790}} {"id":"gen-norm-1127","family":"norm","seed":20260716,"spec":"This 64-feature, 47-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1127\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":226}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":226,\"outFeatures\":156}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":156,\"outFeatures\":342}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":342,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":226}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":156}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":342}}],"grade":{"pass":true,"score":100,"params":119146}} {"id":"gen-tower-1128","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 178-feature user input and a 121-feature item input, each through its own 2-layer MLP tower ending at width 241, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1128\nComponents:\n - user_input (input) params={\"shape\":[1,178]}\n - item_input (input) params={\"shape\":[1,121]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,178]}},{"componentType":"input","name":"item_input","params":{"shape":[1,121]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":178,"outFeatures":241}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":241,"outFeatures":241}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":121,"outFeatures":241}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":241,"outFeatures":241}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":482,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":188703}} {"id":"gen-grow-1129","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (116-feature input, 36 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1129\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":771}},{"type":"update_params","name":"fc2","params":{"inFeatures":771,"outFeatures":771}},{"type":"update_params","name":"head","params":{"inFeatures":771,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":711633}} {"id":"gen-mlp-1130","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 361-feature input with 13 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1130\nComponents:\n - input (input) params={\"shape\":[1,361]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,361]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":361,"outFeatures":734}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":734,"outFeatures":976}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":976,"outFeatures":629}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":629,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1603439}} {"id":"gen-ae-1131","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1474-dim input: encode down to a 42-dim bottleneck and decode back to 1474. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1131\nComponents:\n - input (input) params={\"shape\":[1,1474]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1474]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1474,"outFeatures":278}},{"componentType":"linear","name":"enc2","params":{"inFeatures":278,"outFeatures":42}},{"componentType":"linear","name":"dec1","params":{"inFeatures":42,"outFeatures":278}},{"componentType":"linear","name":"dec2","params":{"inFeatures":278,"outFeatures":1474}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":842896}} {"id":"gen-cnn-1132","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x30x30 image with 82 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1132\nComponents:\n - input (input) params={\"shape\":[1,3,30,30]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,30,30]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":35,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4518}} {"id":"gen-txf-1133","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 501-token sequences with 67 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1133\nComponents:\n - input (input) params={\"shape\":[1,501]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,501]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46675,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":72,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":72,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3427632}} {"id":"gen-gqa-1134","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 315-token sequences with 87 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1134\nComponents:\n - input (input) params={\"shape\":[1,315]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,315]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26213,"embeddingDim":320}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[320]}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":8416000}} {"id":"gen-trim-1136","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3364-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1136\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - fc1 (linear) params={\"inFeatures\":187,\"outFeatures\":3364}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3364,\"outFeatures\":3364}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3364,\"outFeatures\":3364}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3364,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":187,"outFeatures":214}},{"type":"update_params","name":"fc2","params":{"inFeatures":214,"outFeatures":214}},{"type":"update_params","name":"fc3","params":{"inFeatures":214,"outFeatures":214}},{"type":"update_params","name":"head","params":{"inFeatures":214,"outFeatures":15}}],"grade":{"pass":true,"score":84,"params":134820}} {"id":"gen-norm-1137","family":"norm","seed":20260716,"spec":"This 206-feature, 54-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1137\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":145}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":145,\"outFeatures\":152}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":152,\"outFeatures\":439}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":439,\"outFeatures\":312}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":312,\"outFeatures\":437}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":437,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":145}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":152}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":439}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":312}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":437}}],"grade":{"pass":true,"score":100,"params":415548}} {"id":"gen-tower-1138","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 17-feature user input and a 75-feature item input, each through its own 2-layer MLP tower ending at width 52, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1138\nComponents:\n - user_input (input) params={\"shape\":[1,17]}\n - item_input (input) params={\"shape\":[1,75]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,17]}},{"componentType":"input","name":"item_input","params":{"shape":[1,75]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":17,"outFeatures":52}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":52,"outFeatures":52}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":75,"outFeatures":52}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":52,"outFeatures":52}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":10296}} {"id":"gen-grow-1139","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (47-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1139\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":47,"outFeatures":641}},{"type":"update_params","name":"fc2","params":{"inFeatures":641,"outFeatures":641}},{"type":"update_params","name":"head","params":{"inFeatures":641,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":458956}} {"id":"gen-mlp-1140","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 114-feature input with 112 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1140\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,114]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":114,"outFeatures":969}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":969,"outFeatures":603}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":603,"outFeatures":215}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":215,"outFeatures":112}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":848498}} {"id":"gen-ae-1141","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 643-dim input: encode down to a 117-dim bottleneck and decode back to 643. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1141\nComponents:\n - input (input) params={\"shape\":[1,643]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,643]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":643,"outFeatures":635}},{"componentType":"linear","name":"enc2","params":{"inFeatures":635,"outFeatures":117}},{"componentType":"linear","name":"dec1","params":{"inFeatures":117,"outFeatures":635}},{"componentType":"linear","name":"dec2","params":{"inFeatures":635,"outFeatures":643}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":965200}} {"id":"gen-cnn-1142","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 83 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1142\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":63,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":46,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":82343}} {"id":"gen-txf-1143","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 78-token sequences with 39 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1143\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,78]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45382,"embeddingDim":68}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":68,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":68,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":68,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":68,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3144116}} {"id":"gen-gqa-1144","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 315-token sequences with 89 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1144\nComponents:\n - input (input) params={\"shape\":[1,315]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,315]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47744,"embeddingDim":152}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":152,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[152]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":152,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[152]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":152,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[152]}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7270616}} {"id":"gen-trim-1146","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2132-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1146\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - fc1 (linear) params={\"inFeatures\":100,\"outFeatures\":2132}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2132,\"outFeatures\":2132}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2132,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":100,"outFeatures":123}},{"type":"update_params","name":"fc2","params":{"inFeatures":123,"outFeatures":123}},{"type":"update_params","name":"head","params":{"inFeatures":123,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":32103}} {"id":"gen-norm-1147","family":"norm","seed":20260716,"spec":"This 252-feature, 41-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1147\nComponents:\n - input (input) params={\"shape\":[1,252]}\n - fc1 (linear) params={\"inFeatures\":252,\"outFeatures\":120}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":120,\"outFeatures\":182}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":182,\"outFeatures\":231}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":231,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":120}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":182}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":231}}],"grade":{"pass":true,"score":100,"params":103593}} {"id":"gen-tower-1148","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 175-feature user input and a 38-feature item input, each through its own 2-layer MLP tower ending at width 325, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1148\nComponents:\n - user_input (input) params={\"shape\":[1,175]}\n - item_input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,175]}},{"componentType":"input","name":"item_input","params":{"shape":[1,38]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":175,"outFeatures":325}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":38,"outFeatures":325}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":650,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":281125}} {"id":"gen-grow-1149","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (77-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1149\nComponents:\n - input (input) params={\"shape\":[1,77]}\n - fc1 (linear) params={\"inFeatures\":77,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":77,"outFeatures":684}},{"type":"update_params","name":"fc2","params":{"inFeatures":684,"outFeatures":684}},{"type":"update_params","name":"head","params":{"inFeatures":684,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":546516}} {"id":"gen-mlp-1150","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 71-feature input with 91 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1150\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,71]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":71,"outFeatures":199}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":199,"outFeatures":520}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":520,"outFeatures":272}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":283801}} {"id":"gen-ae-1151","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1537-dim input: encode down to a 43-dim bottleneck and decode back to 1537. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1151\nComponents:\n - input (input) params={\"shape\":[1,1537]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1537]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1537,"outFeatures":887}},{"componentType":"linear","name":"enc2","params":{"inFeatures":887,"outFeatures":43}},{"componentType":"linear","name":"dec1","params":{"inFeatures":43,"outFeatures":887}},{"componentType":"linear","name":"dec2","params":{"inFeatures":887,"outFeatures":1537}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2802920}} {"id":"gen-cnn-1152","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 3 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1152\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":50,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":46,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":50,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":60870}} {"id":"gen-txf-1153","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 80-token sequences with 54 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1153\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,80]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24000,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2544880}} {"id":"gen-gqa-1154","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 414-token sequences with 51 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1154\nComponents:\n - input (input) params={\"shape\":[1,414]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,414]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12900,"embeddingDim":320}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[320]}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4144320}} {"id":"gen-fix-1155","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (426) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1155\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - embed (embedding) params={\"numEmbeddings\":42997,\"embeddingDim\":426}\n - attn (multiHeadAttention) params={\"embedDim\":426,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":426,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":19053276}} {"id":"gen-trim-1156","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2266-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1156\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - fc1 (linear) params={\"inFeatures\":240,\"outFeatures\":2266}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2266,\"outFeatures\":2266}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2266,\"outFeatures\":2266}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2266,\"outFeatures\":2266}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2266,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":240,"outFeatures":191}},{"type":"update_params","name":"fc2","params":{"inFeatures":191,"outFeatures":191}},{"type":"update_params","name":"fc3","params":{"inFeatures":191,"outFeatures":191}},{"type":"update_params","name":"fc4","params":{"inFeatures":191,"outFeatures":191}},{"type":"update_params","name":"head","params":{"inFeatures":191,"outFeatures":47}}],"grade":{"pass":true,"score":88,"params":164260}} {"id":"gen-norm-1157","family":"norm","seed":20260716,"spec":"This 117-feature, 58-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1157\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":364}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":364,\"outFeatures\":298}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":298,\"outFeatures\":58}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":364}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":298}}],"grade":{"pass":true,"score":94,"params":168344}} {"id":"gen-tower-1158","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 67-feature user input and a 80-feature item input, each through its own 2-layer MLP tower ending at width 377, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1158\nComponents:\n - user_input (input) params={\"shape\":[1,67]}\n - item_input (input) params={\"shape\":[1,80]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,67]}},{"componentType":"input","name":"item_input","params":{"shape":[1,80]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":67,"outFeatures":377}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":377,"outFeatures":377}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":80,"outFeatures":377}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":377,"outFeatures":377}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":754,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":340431}} {"id":"gen-grow-1159","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (73-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1159\nComponents:\n - input (input) params={\"shape\":[1,73]}\n - fc1 (linear) params={\"inFeatures\":73,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":73,"outFeatures":1227}},{"type":"update_params","name":"fc2","params":{"inFeatures":1227,"outFeatures":1227}},{"type":"update_params","name":"head","params":{"inFeatures":1227,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":1601235}} {"id":"gen-mlp-1160","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 470-feature input with 122 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1160\nComponents:\n - input (input) params={\"shape\":[1,470]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,470]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":470,"outFeatures":212}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":212,"outFeatures":22}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":22,"outFeatures":135}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":135,"outFeatures":754}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":754,"outFeatures":636}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":636,"outFeatures":122}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":766200}} {"id":"gen-ae-1161","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1886-dim input: encode down to a 75-dim bottleneck and decode back to 1886. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1161\nComponents:\n - input (input) params={\"shape\":[1,1886]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1886]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1886,"outFeatures":851}},{"componentType":"linear","name":"enc2","params":{"inFeatures":851,"outFeatures":75}},{"componentType":"linear","name":"dec1","params":{"inFeatures":75,"outFeatures":851}},{"componentType":"linear","name":"dec2","params":{"inFeatures":851,"outFeatures":1886}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3337622}} {"id":"gen-cnn-1162","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 37 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1162\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":30,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":17388}} {"id":"gen-txf-1163","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 34-token sequences with 2 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1163\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,34]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46559,"embeddingDim":56}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":56,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":56,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":56,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2645048}} {"id":"gen-gqa-1164","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 212-token sequences with 22 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1164\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,212]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40366,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5492768}} {"id":"gen-trim-1166","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1979-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1166\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - fc1 (linear) params={\"inFeatures\":235,\"outFeatures\":1979}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1979,\"outFeatures\":1979}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1979,\"outFeatures\":1979}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1979,\"outFeatures\":1979}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1979,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":235,"outFeatures":314}},{"type":"update_params","name":"fc2","params":{"inFeatures":314,"outFeatures":314}},{"type":"update_params","name":"fc3","params":{"inFeatures":314,"outFeatures":314}},{"type":"update_params","name":"fc4","params":{"inFeatures":314,"outFeatures":314}},{"type":"update_params","name":"head","params":{"inFeatures":314,"outFeatures":38}}],"grade":{"pass":true,"score":88,"params":381510}} {"id":"gen-norm-1167","family":"norm","seed":20260716,"spec":"This 210-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1167\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - fc1 (linear) params={\"inFeatures\":210,\"outFeatures\":274}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":274,\"outFeatures\":464}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":464,\"outFeatures\":247}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":247,\"outFeatures\":101}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":101,\"outFeatures\":389}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":389,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":274}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":464}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":247}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":101}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":389}}],"grade":{"pass":true,"score":100,"params":381025}} {"id":"gen-tower-1168","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 93-feature user input and a 83-feature item input, each through its own 2-layer MLP tower ending at width 448, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1168\nComponents:\n - user_input (input) params={\"shape\":[1,93]}\n - item_input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,93]}},{"componentType":"input","name":"item_input","params":{"shape":[1,83]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":93,"outFeatures":448}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":83,"outFeatures":448}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":896,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":481152}} {"id":"gen-grow-1169","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (96-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1169\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":96,"outFeatures":1126}},{"type":"update_params","name":"fc2","params":{"inFeatures":1126,"outFeatures":1126}},{"type":"update_params","name":"head","params":{"inFeatures":1126,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":1423264}} {"id":"gen-mlp-1170","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 237-feature input with 34 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1170\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,237]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":237,"outFeatures":989}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":989,"outFeatures":620}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":620,"outFeatures":236}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":236,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1001917}} {"id":"gen-ae-1171","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 281-dim input: encode down to a 8-dim bottleneck and decode back to 281. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1171\nComponents:\n - input (input) params={\"shape\":[1,281]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,281]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":281,"outFeatures":110}},{"componentType":"linear","name":"enc2","params":{"inFeatures":110,"outFeatures":8}},{"componentType":"linear","name":"dec1","params":{"inFeatures":8,"outFeatures":110}},{"componentType":"linear","name":"dec2","params":{"inFeatures":110,"outFeatures":281}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":63580}} {"id":"gen-cnn-1172","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 65 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1172\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":36,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":23396}} {"id":"gen-txf-1173","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 377-token sequences with 66 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1173\nComponents:\n - input (input) params={\"shape\":[1,377]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,377]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35517,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2824146}} {"id":"gen-gqa-1174","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 457-token sequences with 40 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1174\nComponents:\n - input (input) params={\"shape\":[1,457]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,457]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29169,"embeddingDim":112}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":112,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[112]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":112,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[112]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":112,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[112]}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3271408}} {"id":"gen-trim-1176","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3074-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1176\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":3074}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3074,\"outFeatures\":3074}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3074,\"outFeatures\":3074}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3074,\"outFeatures\":3074}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3074,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":121,"outFeatures":108}},{"type":"update_params","name":"fc2","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"fc3","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"fc4","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"head","params":{"inFeatures":108,"outFeatures":30}}],"grade":{"pass":true,"score":88,"params":51300}} {"id":"gen-norm-1177","family":"norm","seed":20260716,"spec":"This 192-feature, 17-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1177\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - fc1 (linear) params={\"inFeatures\":192,\"outFeatures\":418}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":418,\"outFeatures\":304}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":304,\"outFeatures\":134}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":134,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":418}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":304}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":134}}],"grade":{"pass":true,"score":100,"params":250342}} {"id":"gen-tower-1178","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 238-feature user input and a 24-feature item input, each through its own 2-layer MLP tower ending at width 296, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1178\nComponents:\n - user_input (input) params={\"shape\":[1,238]}\n - item_input (input) params={\"shape\":[1,24]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,238]}},{"componentType":"input","name":"item_input","params":{"shape":[1,24]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":238,"outFeatures":296}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":296,"outFeatures":296}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":24,"outFeatures":296}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":296,"outFeatures":296}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":592,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":253376}} {"id":"gen-grow-1179","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (38-feature input, 47 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1179\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":1126}},{"type":"update_params","name":"fc2","params":{"inFeatures":1126,"outFeatures":1126}},{"type":"update_params","name":"head","params":{"inFeatures":1126,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":1363586}} {"id":"gen-mlp-1180","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 37-feature input with 56 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1180\nComponents:\n - input (input) params={\"shape\":[1,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,37]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":37,"outFeatures":477}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":477,"outFeatures":801}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":801,"outFeatures":334}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":334,"outFeatures":969}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":969,"outFeatures":117}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":117,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1110831}} {"id":"gen-ae-1181","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 887-dim input: encode down to a 14-dim bottleneck and decode back to 887. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1181\nComponents:\n - input (input) params={\"shape\":[1,887]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,887]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":887,"outFeatures":693}},{"componentType":"linear","name":"enc2","params":{"inFeatures":693,"outFeatures":14}},{"componentType":"linear","name":"dec1","params":{"inFeatures":14,"outFeatures":693}},{"componentType":"linear","name":"dec2","params":{"inFeatures":693,"outFeatures":887}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1248786}} {"id":"gen-cnn-1182","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 84 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1182\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":35,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":84}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30312}} {"id":"gen-txf-1183","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 281-token sequences with 9 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1183\nComponents:\n - input (input) params={\"shape\":[1,281]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,281]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38350,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":104,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4119128}} {"id":"gen-gqa-1184","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 276-token sequences with 88 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1184\nComponents:\n - input (input) params={\"shape\":[1,276]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,276]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26462,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":272,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7221600}} {"id":"gen-trim-1186","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2481-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1186\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":2481}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2481,\"outFeatures\":2481}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2481,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":127,"outFeatures":224}},{"type":"update_params","name":"fc2","params":{"inFeatures":224,"outFeatures":224}},{"type":"update_params","name":"head","params":{"inFeatures":224,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":87808}} {"id":"gen-norm-1187","family":"norm","seed":20260716,"spec":"This 256-feature, 17-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1187\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - fc1 (linear) params={\"inFeatures\":256,\"outFeatures\":395}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":395,\"outFeatures\":296}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":296,\"outFeatures\":133}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":133,\"outFeatures\":359}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":359,\"outFeatures\":353}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":353,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":395}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":296}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":133}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":359}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":353}}],"grade":{"pass":true,"score":100,"params":437883}} {"id":"gen-tower-1188","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 58-feature user input and a 182-feature item input, each through its own 2-layer MLP tower ending at width 119, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1188\nComponents:\n - user_input (input) params={\"shape\":[1,58]}\n - item_input (input) params={\"shape\":[1,182]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,58]}},{"componentType":"input","name":"item_input","params":{"shape":[1,182]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":58,"outFeatures":119}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":119,"outFeatures":119}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":182,"outFeatures":119}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":119,"outFeatures":119}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":238,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":57120}} {"id":"gen-grow-1189","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (69-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1189\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - fc1 (linear) params={\"inFeatures\":69,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":69,"outFeatures":956}},{"type":"update_params","name":"fc2","params":{"inFeatures":956,"outFeatures":956}},{"type":"update_params","name":"head","params":{"inFeatures":956,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":995196}} {"id":"gen-mlp-1190","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 326-feature input with 130 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1190\nComponents:\n - input (input) params={\"shape\":[1,326]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,326]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":326,"outFeatures":891}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":891,"outFeatures":353}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":353,"outFeatures":366}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":366,"outFeatures":698}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":698,"outFeatures":130}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1080395}} {"id":"gen-ae-1191","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1089-dim input: encode down to a 57-dim bottleneck and decode back to 1089. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1191\nComponents:\n - input (input) params={\"shape\":[1,1089]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1089]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1089,"outFeatures":687}},{"componentType":"linear","name":"enc2","params":{"inFeatures":687,"outFeatures":57}},{"componentType":"linear","name":"dec1","params":{"inFeatures":57,"outFeatures":687}},{"componentType":"linear","name":"dec2","params":{"inFeatures":687,"outFeatures":1089}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1574604}} {"id":"gen-cnn-1192","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 43 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1192\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":35,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30674}} {"id":"gen-txf-1193","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 353-token sequences with 3 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1193\nComponents:\n - input (input) params={\"shape\":[1,353]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,353]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18764,"embeddingDim":24}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":24,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":452712}} {"id":"gen-gqa-1194","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 505-token sequences with 75 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1194\nComponents:\n - input (input) params={\"shape\":[1,505]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,505]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18409,"embeddingDim":640}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":640,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":640,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[640]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":640,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[640]}},{"componentType":"linear","name":"head","params":{"inFeatures":640,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11829760}} {"id":"gen-fix-1195","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (434) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1195\nComponents:\n - input (input) params={\"shape\":[1,412]}\n - embed (embedding) params={\"numEmbeddings\":34168,\"embeddingDim\":434}\n - attn (multiHeadAttention) params={\"embedDim\":434,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":434,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":15583204}} {"id":"gen-trim-1196","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3690-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1196\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":3690}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3690,\"outFeatures\":3690}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3690,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":269}},{"type":"update_params","name":"fc2","params":{"inFeatures":269,"outFeatures":269}},{"type":"update_params","name":"head","params":{"inFeatures":269,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":99261}} {"id":"gen-norm-1197","family":"norm","seed":20260716,"spec":"This 166-feature, 76-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1197\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - fc1 (linear) params={\"inFeatures\":166,\"outFeatures\":418}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":418,\"outFeatures\":367}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":367,\"outFeatures\":288}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":288,\"outFeatures\":231}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":231,\"outFeatures\":91}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":91,\"outFeatures\":76}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":418}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":367}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":288}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":231}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":91}}],"grade":{"pass":true,"score":100,"params":422955}} {"id":"gen-tower-1198","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 83-feature user input and a 134-feature item input, each through its own 2-layer MLP tower ending at width 238, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1198\nComponents:\n - user_input (input) params={\"shape\":[1,83]}\n - item_input (input) params={\"shape\":[1,134]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,83]}},{"componentType":"input","name":"item_input","params":{"shape":[1,134]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":83,"outFeatures":238}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":238,"outFeatures":238}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":134,"outFeatures":238}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":238,"outFeatures":238}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":476,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":165410}} {"id":"gen-grow-1199","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (50-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1199\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":690}},{"type":"update_params","name":"fc2","params":{"inFeatures":690,"outFeatures":690}},{"type":"update_params","name":"head","params":{"inFeatures":690,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":538890}} {"id":"gen-mlp-1200","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 318-feature input with 3 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1200\nComponents:\n - input (input) params={\"shape\":[1,318]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,318]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":318,"outFeatures":905}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":905,"outFeatures":680}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":680,"outFeatures":181}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":181,"outFeatures":344}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1089566}} {"id":"gen-ae-1201","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1668-dim input: encode down to a 30-dim bottleneck and decode back to 1668. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1201\nComponents:\n - input (input) params={\"shape\":[1,1668]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1668]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1668,"outFeatures":624}},{"componentType":"linear","name":"enc2","params":{"inFeatures":624,"outFeatures":30}},{"componentType":"linear","name":"dec1","params":{"inFeatures":30,"outFeatures":624}},{"componentType":"linear","name":"dec2","params":{"inFeatures":624,"outFeatures":1668}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2119104}} {"id":"gen-cnn-1202","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 20 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1202\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":6492}} {"id":"gen-txf-1203","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 111-token sequences with 82 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1203\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,111]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22196,"embeddingDim":24}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":24,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":536976}} {"id":"gen-gqa-1204","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 146-token sequences with 63 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1204\nComponents:\n - input (input) params={\"shape\":[1,146]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,146]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12892,"embeddingDim":280}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":280,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[280]}},{"componentType":"linear","name":"head","params":{"inFeatures":280,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3627400}} {"id":"gen-fix-1205","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (402) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1205\nComponents:\n - input (input) params={\"shape\":[1,376]}\n - embed (embedding) params={\"numEmbeddings\":34892,\"embeddingDim\":402}\n - attn (multiHeadAttention) params={\"embedDim\":402,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":402,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":14702748}} {"id":"gen-trim-1206","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2808-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1206\nComponents:\n - input (input) params={\"shape\":[1,112]}\n - fc1 (linear) params={\"inFeatures\":112,\"outFeatures\":2808}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2808,\"outFeatures\":2808}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2808,\"outFeatures\":2808}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2808,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":112,"outFeatures":159}},{"type":"update_params","name":"fc2","params":{"inFeatures":159,"outFeatures":159}},{"type":"update_params","name":"fc3","params":{"inFeatures":159,"outFeatures":159}},{"type":"update_params","name":"head","params":{"inFeatures":159,"outFeatures":33}}],"grade":{"pass":true,"score":84,"params":73617}} {"id":"gen-norm-1207","family":"norm","seed":20260716,"spec":"This 190-feature, 96-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1207\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":207}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":207,\"outFeatures\":276}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":276,\"outFeatures\":509}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":509,\"outFeatures\":409}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":409,\"outFeatures\":467}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":467,\"outFeatures\":96}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":207}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":509}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":467}}],"grade":{"pass":true,"score":100,"params":680962}} {"id":"gen-tower-1208","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 187-feature user input and a 83-feature item input, each through its own 2-layer MLP tower ending at width 241, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1208\nComponents:\n - user_input (input) params={\"shape\":[1,187]}\n - item_input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,187]}},{"componentType":"input","name":"item_input","params":{"shape":[1,83]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":187,"outFeatures":241}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":241,"outFeatures":241}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":83,"outFeatures":241}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":241,"outFeatures":241}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":482,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":181714}} {"id":"gen-mlp-1210","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 402-feature input with 27 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1210\nComponents:\n - input (input) params={\"shape\":[1,402]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,402]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":402,"outFeatures":454}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":454,"outFeatures":216}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":216,"outFeatures":974}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":974,"outFeatures":695}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":695,"outFeatures":438}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":438,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1484122}} {"id":"gen-ae-1211","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1939-dim input: encode down to a 58-dim bottleneck and decode back to 1939. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1211\nComponents:\n - input (input) params={\"shape\":[1,1939]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1939]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1939,"outFeatures":544}},{"componentType":"linear","name":"enc2","params":{"inFeatures":544,"outFeatures":58}},{"componentType":"linear","name":"dec1","params":{"inFeatures":58,"outFeatures":544}},{"componentType":"linear","name":"dec2","params":{"inFeatures":544,"outFeatures":1939}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2172736}} {"id":"gen-cnn-1212","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 50 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1212\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":21905}} {"id":"gen-txf-1213","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 83-token sequences with 55 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1213\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,83]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37850,"embeddingDim":344}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":344,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":344,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":13986008}} {"id":"gen-gqa-1214","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 370-token sequences with 42 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1214\nComponents:\n - input (input) params={\"shape\":[1,370]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,370]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34494,"embeddingDim":480}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":480,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[480]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":480,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[480]}},{"componentType":"linear","name":"head","params":{"inFeatures":480,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":16577280}} {"id":"gen-fix-1215","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (68) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1215\nComponents:\n - input (input) params={\"shape\":[1,268]}\n - embed (embedding) params={\"numEmbeddings\":13277,\"embeddingDim\":68}\n - attn (multiHeadAttention) params={\"embedDim\":68,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":68,\"outFeatures\":66}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":925820}} {"id":"gen-trim-1216","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1596-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1216\nComponents:\n - input (input) params={\"shape\":[1,160]}\n - fc1 (linear) params={\"inFeatures\":160,\"outFeatures\":1596}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1596,\"outFeatures\":1596}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1596,\"outFeatures\":1596}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1596,\"outFeatures\":1596}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1596,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":160,"outFeatures":219}},{"type":"update_params","name":"fc2","params":{"inFeatures":219,"outFeatures":219}},{"type":"update_params","name":"fc3","params":{"inFeatures":219,"outFeatures":219}},{"type":"update_params","name":"fc4","params":{"inFeatures":219,"outFeatures":219}},{"type":"update_params","name":"head","params":{"inFeatures":219,"outFeatures":27}}],"grade":{"pass":true,"score":88,"params":184836}} {"id":"gen-norm-1217","family":"norm","seed":20260716,"spec":"This 92-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1217\nComponents:\n - input (input) params={\"shape\":[1,92]}\n - fc1 (linear) params={\"inFeatures\":92,\"outFeatures\":276}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":276,\"outFeatures\":374}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":374,\"outFeatures\":351}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":351,\"outFeatures\":93}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":93,\"outFeatures\":41}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":41,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":374}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":351}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":93}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":41}}],"grade":{"pass":true,"score":100,"params":297904}} {"id":"gen-tower-1218","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 142-feature user input and a 169-feature item input, each through its own 2-layer MLP tower ending at width 445, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1218\nComponents:\n - user_input (input) params={\"shape\":[1,142]}\n - item_input (input) params={\"shape\":[1,169]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,142]}},{"componentType":"input","name":"item_input","params":{"shape":[1,169]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":142,"outFeatures":445}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":445,"outFeatures":445}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":169,"outFeatures":445}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":445,"outFeatures":445}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":890,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":535335}} {"id":"gen-grow-1219","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (30-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1219\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":30,"outFeatures":650}},{"type":"update_params","name":"fc2","params":{"inFeatures":650,"outFeatures":650}},{"type":"update_params","name":"head","params":{"inFeatures":650,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":460200}} {"id":"gen-mlp-1220","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 196-feature input with 20 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1220\nComponents:\n - input (input) params={\"shape\":[1,196]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,196]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":196,"outFeatures":953}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":953,"outFeatures":831}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":831,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":995351}} {"id":"gen-ae-1221","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1382-dim input: encode down to a 30-dim bottleneck and decode back to 1382. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1221\nComponents:\n - input (input) params={\"shape\":[1,1382]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1382]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1382,"outFeatures":301}},{"componentType":"linear","name":"enc2","params":{"inFeatures":301,"outFeatures":30}},{"componentType":"linear","name":"dec1","params":{"inFeatures":30,"outFeatures":301}},{"componentType":"linear","name":"dec2","params":{"inFeatures":301,"outFeatures":1382}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":850024}} {"id":"gen-cnn-1222","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 94 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1222\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":48,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":13,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":12913}} {"id":"gen-txf-1223","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 295-token sequences with 70 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1223\nComponents:\n - input (input) params={\"shape\":[1,295]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,295]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16003,"embeddingDim":416}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":416,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":416,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":416,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":8763040}} {"id":"gen-gqa-1224","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 112-token sequences with 31 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1224\nComponents:\n - input (input) params={\"shape\":[1,112]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,112]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18767,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":80,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[80]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":80,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1503840}} {"id":"gen-fix-1225","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (434) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1225\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - embed (embedding) params={\"numEmbeddings\":17125,\"embeddingDim\":434}\n - attn (multiHeadAttention) params={\"embedDim\":434,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":434,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":8191316}} {"id":"gen-trim-1226","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2223-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1226\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - fc1 (linear) params={\"inFeatures\":225,\"outFeatures\":2223}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2223,\"outFeatures\":2223}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2223,\"outFeatures\":2223}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2223,\"outFeatures\":2223}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2223,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":225,"outFeatures":273}},{"type":"update_params","name":"fc2","params":{"inFeatures":273,"outFeatures":273}},{"type":"update_params","name":"fc3","params":{"inFeatures":273,"outFeatures":273}},{"type":"update_params","name":"fc4","params":{"inFeatures":273,"outFeatures":273}},{"type":"update_params","name":"head","params":{"inFeatures":273,"outFeatures":18}}],"grade":{"pass":true,"score":88,"params":289926}} {"id":"gen-norm-1227","family":"norm","seed":20260716,"spec":"This 107-feature, 8-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1227\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":417}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":417,\"outFeatures\":169}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":169,\"outFeatures\":47}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":47,\"outFeatures\":263}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":263,\"outFeatures\":315}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":315,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":417}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":169}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":47}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":263}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":315}}],"grade":{"pass":true,"score":100,"params":220761}} {"id":"gen-tower-1228","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 83-feature user input and a 32-feature item input, each through its own 2-layer MLP tower ending at width 399, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1228\nComponents:\n - user_input (input) params={\"shape\":[1,83]}\n - item_input (input) params={\"shape\":[1,32]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,83]}},{"componentType":"input","name":"item_input","params":{"shape":[1,32]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":83,"outFeatures":399}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":399,"outFeatures":399}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":32,"outFeatures":399}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":399,"outFeatures":399}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":798,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":365085}} {"id":"gen-grow-1229","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1229\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":646}},{"type":"update_params","name":"fc2","params":{"inFeatures":646,"outFeatures":646}},{"type":"update_params","name":"head","params":{"inFeatures":646,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":492898}} {"id":"gen-mlp-1230","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 248-feature input with 56 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1230\nComponents:\n - input (input) params={\"shape\":[1,248]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,248]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":248,"outFeatures":910}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":910,"outFeatures":780}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":780,"outFeatures":606}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":606,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1442096}} {"id":"gen-ae-1231","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1048-dim input: encode down to a 158-dim bottleneck and decode back to 1048. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1231\nComponents:\n - input (input) params={\"shape\":[1,1048]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1048]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1048,"outFeatures":737}},{"componentType":"linear","name":"enc2","params":{"inFeatures":737,"outFeatures":158}},{"componentType":"linear","name":"dec1","params":{"inFeatures":158,"outFeatures":737}},{"componentType":"linear","name":"dec2","params":{"inFeatures":737,"outFeatures":1048}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1777644}} {"id":"gen-cnn-1232","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x57x57 image with 89 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1232\nComponents:\n - input (input) params={\"shape\":[1,3,57,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,57,57]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":60,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":37005}} {"id":"gen-txf-1233","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 280-token sequences with 96 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1233\nComponents:\n - input (input) params={\"shape\":[1,280]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,280]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25500,"embeddingDim":264}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":264,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":7036128}} {"id":"gen-gqa-1234","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 378-token sequences with 57 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1234\nComponents:\n - input (input) params={\"shape\":[1,378]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,378]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31559,"embeddingDim":88}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":88,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[88]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":88,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[88]}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2782208}} {"id":"gen-trim-1236","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3399-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1236\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - fc1 (linear) params={\"inFeatures\":237,\"outFeatures\":3399}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3399,\"outFeatures\":3399}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3399,\"outFeatures\":3399}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3399,\"outFeatures\":3399}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3399,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":237,"outFeatures":358}},{"type":"update_params","name":"fc2","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"fc3","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"fc4","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"head","params":{"inFeatures":358,"outFeatures":21}}],"grade":{"pass":true,"score":88,"params":476856}} {"id":"gen-norm-1237","family":"norm","seed":20260716,"spec":"This 242-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1237\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - fc1 (linear) params={\"inFeatures\":242,\"outFeatures\":167}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":167,\"outFeatures\":34}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":34,\"outFeatures\":435}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":435,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":167}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":34}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":435}}],"grade":{"pass":true,"score":100,"params":94812}} {"id":"gen-tower-1238","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 90-feature user input and a 112-feature item input, each through its own 2-layer MLP tower ending at width 369, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1238\nComponents:\n - user_input (input) params={\"shape\":[1,90]}\n - item_input (input) params={\"shape\":[1,112]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,90]}},{"componentType":"input","name":"item_input","params":{"shape":[1,112]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":90,"outFeatures":369}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":369,"outFeatures":369}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":112,"outFeatures":369}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":369,"outFeatures":369}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":738,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":347598}} {"id":"gen-grow-1239","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (110-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1239\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":110,"outFeatures":1201}},{"type":"update_params","name":"fc2","params":{"inFeatures":1201,"outFeatures":1201}},{"type":"update_params","name":"head","params":{"inFeatures":1201,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1606938}} {"id":"gen-mlp-1240","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 128-feature input with 91 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1240\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":128,"outFeatures":271}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":271,"outFeatures":784}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":784,"outFeatures":731}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":731,"outFeatures":942}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":942,"outFeatures":832}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":832,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2368314}} {"id":"gen-ae-1241","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 419-dim input: encode down to a 105-dim bottleneck and decode back to 419. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1241\nComponents:\n - input (input) params={\"shape\":[1,419]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,419]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":419,"outFeatures":723}},{"componentType":"linear","name":"enc2","params":{"inFeatures":723,"outFeatures":105}},{"componentType":"linear","name":"dec1","params":{"inFeatures":105,"outFeatures":723}},{"componentType":"linear","name":"dec2","params":{"inFeatures":723,"outFeatures":419}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":757704}} {"id":"gen-cnn-1242","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 61 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1242\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":61}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":8833}} {"id":"gen-txf-1243","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 445-token sequences with 83 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1243\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10366,"embeddingDim":76}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":76,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":76,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":840332}} {"id":"gen-gqa-1244","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 390-token sequences with 74 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1244\nComponents:\n - input (input) params={\"shape\":[1,390]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,390]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9626,"embeddingDim":432}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":432,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[432]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":432,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[432]}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4190400}} {"id":"gen-fix-1245","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (212) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1245\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - embed (embedding) params={\"numEmbeddings\":29422,\"embeddingDim\":212}\n - attn (multiHeadAttention) params={\"embedDim\":212,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":212,\"outFeatures\":80}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":6434200}} {"id":"gen-trim-1246","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2500-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1246\nComponents:\n - input (input) params={\"shape\":[1,215]}\n - fc1 (linear) params={\"inFeatures\":215,\"outFeatures\":2500}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2500,\"outFeatures\":2500}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2500,\"outFeatures\":2500}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2500,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":215,"outFeatures":216}},{"type":"update_params","name":"fc2","params":{"inFeatures":216,"outFeatures":216}},{"type":"update_params","name":"fc3","params":{"inFeatures":216,"outFeatures":216}},{"type":"update_params","name":"head","params":{"inFeatures":216,"outFeatures":17}}],"grade":{"pass":true,"score":84,"params":143424}} {"id":"gen-norm-1247","family":"norm","seed":20260716,"spec":"This 162-feature, 99-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1247\nComponents:\n - input (input) params={\"shape\":[1,162]}\n - fc1 (linear) params={\"inFeatures\":162,\"outFeatures\":362}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":362,\"outFeatures\":234}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":234,\"outFeatures\":380}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":380,\"outFeatures\":478}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":478,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":362}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":234}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":380}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":478}}],"grade":{"pass":true,"score":100,"params":461234}} {"id":"gen-tower-1248","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 130-feature user input and a 59-feature item input, each through its own 2-layer MLP tower ending at width 476, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1248\nComponents:\n - user_input (input) params={\"shape\":[1,130]}\n - item_input (input) params={\"shape\":[1,59]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,130]}},{"componentType":"input","name":"item_input","params":{"shape":[1,59]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":130,"outFeatures":476}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":476,"outFeatures":476}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":59,"outFeatures":476}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":476,"outFeatures":476}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":952,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":544068}} {"id":"gen-grow-1249","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (17-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1249\nComponents:\n - input (input) params={\"shape\":[1,17]}\n - fc1 (linear) params={\"inFeatures\":17,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":17,"outFeatures":927}},{"type":"update_params","name":"fc2","params":{"inFeatures":927,"outFeatures":927}},{"type":"update_params","name":"head","params":{"inFeatures":927,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":888066}} {"id":"gen-mlp-1250","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 209-feature input with 149 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1250\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,209]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":209,"outFeatures":891}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":891,"outFeatures":591}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":591,"outFeatures":25}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":149}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":731300}} {"id":"gen-ae-1251","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 965-dim input: encode down to a 64-dim bottleneck and decode back to 965. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1251\nComponents:\n - input (input) params={\"shape\":[1,965]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,965]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":965,"outFeatures":851}},{"componentType":"linear","name":"enc2","params":{"inFeatures":851,"outFeatures":64}},{"componentType":"linear","name":"dec1","params":{"inFeatures":64,"outFeatures":851}},{"componentType":"linear","name":"dec2","params":{"inFeatures":851,"outFeatures":965}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1751358}} {"id":"gen-cnn-1252","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 49 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1252\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":45,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":31951}} {"id":"gen-txf-1253","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 508-token sequences with 20 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1253\nComponents:\n - input (input) params={\"shape\":[1,508]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,508]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8587,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":240,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":240,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2756880}} {"id":"gen-gqa-1254","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 372-token sequences with 100 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1254\nComponents:\n - input (input) params={\"shape\":[1,372]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,372]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24456,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5500544}} {"id":"gen-trim-1256","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2398-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1256\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":2398}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2398,\"outFeatures\":2398}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2398,\"outFeatures\":2398}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2398,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":149}},{"type":"update_params","name":"fc2","params":{"inFeatures":149,"outFeatures":149}},{"type":"update_params","name":"fc3","params":{"inFeatures":149,"outFeatures":149}},{"type":"update_params","name":"head","params":{"inFeatures":149,"outFeatures":19}}],"grade":{"pass":true,"score":84,"params":63325}} {"id":"gen-norm-1257","family":"norm","seed":20260716,"spec":"This 128-feature, 52-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1257\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":389}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":389,\"outFeatures\":109}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":109,\"outFeatures\":259}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":259,\"outFeatures\":52}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":389}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":109}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":259}}],"grade":{"pass":true,"score":100,"params":133892}} {"id":"gen-tower-1258","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 198-feature user input and a 240-feature item input, each through its own 2-layer MLP tower ending at width 259, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1258\nComponents:\n - user_input (input) params={\"shape\":[1,198]}\n - item_input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,198]}},{"componentType":"input","name":"item_input","params":{"shape":[1,240]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":198,"outFeatures":259}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":259,"outFeatures":259}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":240,"outFeatures":259}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":259,"outFeatures":259}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":518,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":248122}} {"id":"gen-grow-1259","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (121-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1259\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":121,"outFeatures":827}},{"type":"update_params","name":"fc2","params":{"inFeatures":827,"outFeatures":827}},{"type":"update_params","name":"head","params":{"inFeatures":827,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":815422}} {"id":"gen-mlp-1260","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 503-feature input with 21 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1260\nComponents:\n - input (input) params={\"shape\":[1,503]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,503]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":503,"outFeatures":188}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":188,"outFeatures":931}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":931,"outFeatures":942}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":942,"outFeatures":451}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":451,"outFeatures":970}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":970,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2029276}} {"id":"gen-ae-1261","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 811-dim input: encode down to a 38-dim bottleneck and decode back to 811. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1261\nComponents:\n - input (input) params={\"shape\":[1,811]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,811]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":811,"outFeatures":133}},{"componentType":"linear","name":"enc2","params":{"inFeatures":133,"outFeatures":38}},{"componentType":"linear","name":"dec1","params":{"inFeatures":38,"outFeatures":133}},{"componentType":"linear","name":"dec2","params":{"inFeatures":133,"outFeatures":811}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":225834}} {"id":"gen-cnn-1262","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x26x26 image with 45 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1262\nComponents:\n - input (input) params={\"shape\":[1,3,26,26]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,26,26]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":40,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":19,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":11376}} {"id":"gen-txf-1263","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 67-token sequences with 30 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1263\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,67]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33379,"embeddingDim":288}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":288,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":30}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":9953568}} {"id":"gen-gqa-1264","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 253-token sequences with 93 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1264\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,253]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10927,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3173760}} {"id":"gen-fix-1265","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (326) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1265\nComponents:\n - input (input) params={\"shape\":[1,312]}\n - embed (embedding) params={\"numEmbeddings\":8782,\"embeddingDim\":326}\n - attn (multiHeadAttention) params={\"embedDim\":326,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":326,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":3297164}} {"id":"gen-trim-1266","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2247-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1266\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - fc1 (linear) params={\"inFeatures\":256,\"outFeatures\":2247}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2247,\"outFeatures\":2247}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2247,\"outFeatures\":2247}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2247,\"outFeatures\":2247}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2247,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":256,"outFeatures":203}},{"type":"update_params","name":"fc2","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"fc3","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"fc4","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"head","params":{"inFeatures":203,"outFeatures":48}}],"grade":{"pass":true,"score":88,"params":185339}} {"id":"gen-norm-1267","family":"norm","seed":20260716,"spec":"This 48-feature, 31-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1267\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":272}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":272,\"outFeatures\":230}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":230,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":272}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":230}}],"grade":{"pass":true,"score":94,"params":82746}} {"id":"gen-tower-1268","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 113-feature item input, each through its own 2-layer MLP tower ending at width 388, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1268\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,113]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":388}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":388,"outFeatures":388}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":113,"outFeatures":388}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":388,"outFeatures":388}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":776,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":438828}} {"id":"gen-grow-1269","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (45-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1269\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - fc1 (linear) params={\"inFeatures\":45,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":45,"outFeatures":1217}},{"type":"update_params","name":"fc2","params":{"inFeatures":1217,"outFeatures":1217}},{"type":"update_params","name":"head","params":{"inFeatures":1217,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1568713}} {"id":"gen-mlp-1270","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 361-feature input with 136 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1270\nComponents:\n - input (input) params={\"shape\":[1,361]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,361]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":361,"outFeatures":485}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":485,"outFeatures":108}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":108,"outFeatures":873}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":873,"outFeatures":397}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":397,"outFeatures":987}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":987,"outFeatures":465}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":465,"outFeatures":136}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1582364}} {"id":"gen-ae-1271","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 506-dim input: encode down to a 56-dim bottleneck and decode back to 506. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1271\nComponents:\n - input (input) params={\"shape\":[1,506]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,506]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":506,"outFeatures":776}},{"componentType":"linear","name":"enc2","params":{"inFeatures":776,"outFeatures":56}},{"componentType":"linear","name":"dec1","params":{"inFeatures":56,"outFeatures":776}},{"componentType":"linear","name":"dec2","params":{"inFeatures":776,"outFeatures":506}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":872224}} {"id":"gen-cnn-1272","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 17 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1272\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":50,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":21,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29377}} {"id":"gen-txf-1273","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 128-token sequences with 44 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1273\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33948,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":72,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2488896}} {"id":"gen-gqa-1274","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 158-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1274\nComponents:\n - input (input) params={\"shape\":[1,158]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,158]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8529,"embeddingDim":264}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":264,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[264]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":264,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[264]}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2271456}} {"id":"gen-fix-1275","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (346) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1275\nComponents:\n - input (input) params={\"shape\":[1,175]}\n - embed (embedding) params={\"numEmbeddings\":29189,\"embeddingDim\":346}\n - attn (multiHeadAttention) params={\"embedDim\":346,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":346,\"outFeatures\":71}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":10602824}} {"id":"gen-trim-1276","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3090-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1276\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":3090}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3090,\"outFeatures\":3090}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3090,\"outFeatures\":3090}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3090,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":122,"outFeatures":134}},{"type":"update_params","name":"fc2","params":{"inFeatures":134,"outFeatures":134}},{"type":"update_params","name":"fc3","params":{"inFeatures":134,"outFeatures":134}},{"type":"update_params","name":"head","params":{"inFeatures":134,"outFeatures":14}}],"grade":{"pass":true,"score":84,"params":54136}} {"id":"gen-norm-1277","family":"norm","seed":20260716,"spec":"This 221-feature, 94-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1277\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - fc1 (linear) params={\"inFeatures\":221,\"outFeatures\":259}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":259,\"outFeatures\":64}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":64,\"outFeatures\":94}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":259}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":64}}],"grade":{"pass":true,"score":94,"params":79831}} {"id":"gen-tower-1278","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 106-feature user input and a 38-feature item input, each through its own 2-layer MLP tower ending at width 163, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1278\nComponents:\n - user_input (input) params={\"shape\":[1,106]}\n - item_input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,106]}},{"componentType":"input","name":"item_input","params":{"shape":[1,38]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":106,"outFeatures":163}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":163,"outFeatures":163}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":38,"outFeatures":163}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":163,"outFeatures":163}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":326,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":76936}} {"id":"gen-grow-1279","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (86-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1279\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - fc1 (linear) params={\"inFeatures\":86,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":86,"outFeatures":803}},{"type":"update_params","name":"fc2","params":{"inFeatures":803,"outFeatures":803}},{"type":"update_params","name":"head","params":{"inFeatures":803,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":741972}} {"id":"gen-mlp-1280","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 293-feature input with 152 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1280\nComponents:\n - input (input) params={\"shape\":[1,293]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,293]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":293,"outFeatures":409}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":409,"outFeatures":257}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":257,"outFeatures":732}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":732,"outFeatures":801}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":801,"outFeatures":578}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":578,"outFeatures":152}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1550240}} {"id":"gen-ae-1281","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 969-dim input: encode down to a 224-dim bottleneck and decode back to 969. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1281\nComponents:\n - input (input) params={\"shape\":[1,969]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,969]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":969,"outFeatures":187}},{"componentType":"linear","name":"enc2","params":{"inFeatures":187,"outFeatures":224}},{"componentType":"linear","name":"dec1","params":{"inFeatures":224,"outFeatures":187}},{"componentType":"linear","name":"dec2","params":{"inFeatures":187,"outFeatures":969}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":446182}} {"id":"gen-cnn-1282","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 14 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1282\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":57,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":14}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":34437}} {"id":"gen-txf-1283","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 495-token sequences with 2 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1283\nComponents:\n - input (input) params={\"shape\":[1,495]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,495]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20419,"embeddingDim":232}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":232,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4952968}} {"id":"gen-gqa-1284","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 412-token sequences with 70 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1284\nComponents:\n - input (input) params={\"shape\":[1,412]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,412]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23396,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":6382752}} {"id":"gen-fix-1285","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (72) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1285\nComponents:\n - input (input) params={\"shape\":[1,261]}\n - embed (embedding) params={\"numEmbeddings\":46756,\"embeddingDim\":72}\n - attn (multiHeadAttention) params={\"embedDim\":72,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":72,\"outFeatures\":100}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":3394368}} {"id":"gen-trim-1286","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2069-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1286\nComponents:\n - input (input) params={\"shape\":[1,143]}\n - fc1 (linear) params={\"inFeatures\":143,\"outFeatures\":2069}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2069,\"outFeatures\":2069}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2069,\"outFeatures\":2069}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2069,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":143,"outFeatures":196}},{"type":"update_params","name":"fc2","params":{"inFeatures":196,"outFeatures":196}},{"type":"update_params","name":"fc3","params":{"inFeatures":196,"outFeatures":196}},{"type":"update_params","name":"head","params":{"inFeatures":196,"outFeatures":19}}],"grade":{"pass":true,"score":84,"params":108584}} {"id":"gen-norm-1287","family":"norm","seed":20260716,"spec":"This 182-feature, 48-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1287\nComponents:\n - input (input) params={\"shape\":[1,182]}\n - fc1 (linear) params={\"inFeatures\":182,\"outFeatures\":115}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":115,\"outFeatures\":176}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":176,\"outFeatures\":392}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":392,\"outFeatures\":301}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":301,\"outFeatures\":82}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":82,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":115}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":176}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":392}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":301}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":82}}],"grade":{"pass":true,"score":100,"params":256772}} {"id":"gen-tower-1288","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 182-feature user input and a 87-feature item input, each through its own 2-layer MLP tower ending at width 313, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1288\nComponents:\n - user_input (input) params={\"shape\":[1,182]}\n - item_input (input) params={\"shape\":[1,87]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,182]}},{"componentType":"input","name":"item_input","params":{"shape":[1,87]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":182,"outFeatures":313}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":313,"outFeatures":313}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":87,"outFeatures":313}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":313,"outFeatures":313}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":626,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":280761}} {"id":"gen-grow-1289","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1289\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":700}},{"type":"update_params","name":"fc2","params":{"inFeatures":700,"outFeatures":700}},{"type":"update_params","name":"head","params":{"inFeatures":700,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":577500}} {"id":"gen-mlp-1290","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 462-feature input with 42 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1290\nComponents:\n - input (input) params={\"shape\":[1,462]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,462]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":462,"outFeatures":767}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":767,"outFeatures":190}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":190,"outFeatures":700}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":700,"outFeatures":389}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":389,"outFeatures":584}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":584,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1157088}} {"id":"gen-ae-1291","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 994-dim input: encode down to a 210-dim bottleneck and decode back to 994. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1291\nComponents:\n - input (input) params={\"shape\":[1,994]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,994]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":994,"outFeatures":905}},{"componentType":"linear","name":"enc2","params":{"inFeatures":905,"outFeatures":210}},{"componentType":"linear","name":"dec1","params":{"inFeatures":210,"outFeatures":905}},{"componentType":"linear","name":"dec2","params":{"inFeatures":905,"outFeatures":994}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2179240}} {"id":"gen-cnn-1292","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 45 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1292\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":14,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":7200}} {"id":"gen-txf-1293","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 317-token sequences with 44 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1293\nComponents:\n - input (input) params={\"shape\":[1,317]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,317]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46357,"embeddingDim":180}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":180,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":8481780}} {"id":"gen-gqa-1294","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 230-token sequences with 46 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1294\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,230]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14469,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2670760}} {"id":"gen-trim-1296","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1639-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1296\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":1639}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1639,\"outFeatures\":1639}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1639,\"outFeatures\":1639}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1639,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":294}},{"type":"update_params","name":"fc2","params":{"inFeatures":294,"outFeatures":294}},{"type":"update_params","name":"fc3","params":{"inFeatures":294,"outFeatures":294}},{"type":"update_params","name":"head","params":{"inFeatures":294,"outFeatures":27}}],"grade":{"pass":true,"score":84,"params":211386}} {"id":"gen-norm-1297","family":"norm","seed":20260716,"spec":"This 28-feature, 71-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1297\nComponents:\n - input (input) params={\"shape\":[1,28]}\n - fc1 (linear) params={\"inFeatures\":28,\"outFeatures\":194}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":194,\"outFeatures\":283}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":283,\"outFeatures\":71}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":194}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":283}}],"grade":{"pass":true,"score":94,"params":80427}} {"id":"gen-tower-1298","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 156-feature user input and a 213-feature item input, each through its own 2-layer MLP tower ending at width 258, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1298\nComponents:\n - user_input (input) params={\"shape\":[1,156]}\n - item_input (input) params={\"shape\":[1,213]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,156]}},{"componentType":"input","name":"item_input","params":{"shape":[1,213]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":156,"outFeatures":258}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":258,"outFeatures":258}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":213,"outFeatures":258}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":258,"outFeatures":258}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":516,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":228846}} {"id":"gen-grow-1299","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (84-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1299\nComponents:\n - input (input) params={\"shape\":[1,84]}\n - fc1 (linear) params={\"inFeatures\":84,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":84,"outFeatures":1165}},{"type":"update_params","name":"fc2","params":{"inFeatures":1165,"outFeatures":1165}},{"type":"update_params","name":"head","params":{"inFeatures":1165,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":1502850}} {"id":"gen-mlp-1300","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 118-feature input with 198 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1300\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,118]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":118,"outFeatures":268}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":268,"outFeatures":155}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":155,"outFeatures":198}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":103854}} {"id":"gen-ae-1301","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1363-dim input: encode down to a 184-dim bottleneck and decode back to 1363. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1301\nComponents:\n - input (input) params={\"shape\":[1,1363]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1363]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1363,"outFeatures":1010}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1010,"outFeatures":184}},{"componentType":"linear","name":"dec1","params":{"inFeatures":184,"outFeatures":1010}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1010,"outFeatures":1363}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3124940}} {"id":"gen-cnn-1302","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 20 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1302\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":23,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":42962}} {"id":"gen-txf-1303","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 197-token sequences with 22 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1303\nComponents:\n - input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,197]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32140,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":7949280}} {"id":"gen-gqa-1304","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 206-token sequences with 54 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1304\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,206]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23771,"embeddingDim":480}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":480,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[480]}},{"componentType":"linear","name":"head","params":{"inFeatures":480,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":11436000}} {"id":"gen-trim-1306","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2594-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1306\nComponents:\n - input (input) params={\"shape\":[1,40]}\n - fc1 (linear) params={\"inFeatures\":40,\"outFeatures\":2594}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2594,\"outFeatures\":2594}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2594,\"outFeatures\":2594}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2594,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":40,"outFeatures":300}},{"type":"update_params","name":"fc2","params":{"inFeatures":300,"outFeatures":300}},{"type":"update_params","name":"fc3","params":{"inFeatures":300,"outFeatures":300}},{"type":"update_params","name":"head","params":{"inFeatures":300,"outFeatures":39}}],"grade":{"pass":true,"score":84,"params":203700}} {"id":"gen-norm-1307","family":"norm","seed":20260716,"spec":"This 48-feature, 68-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1307\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":368}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":368,\"outFeatures\":465}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":465,\"outFeatures\":237}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":237,\"outFeatures\":55}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":55,\"outFeatures\":68}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":368}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":465}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":237}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":55}}],"grade":{"pass":true,"score":100,"params":315764}} {"id":"gen-tower-1308","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 152-feature user input and a 240-feature item input, each through its own 2-layer MLP tower ending at width 268, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1308\nComponents:\n - user_input (input) params={\"shape\":[1,152]}\n - item_input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,152]}},{"componentType":"input","name":"item_input","params":{"shape":[1,240]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":152,"outFeatures":268}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":268,"outFeatures":268}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":240,"outFeatures":268}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":268,"outFeatures":268}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":536,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":249240}} {"id":"gen-grow-1309","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (81-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1309\nComponents:\n - input (input) params={\"shape\":[1,81]}\n - fc1 (linear) params={\"inFeatures\":81,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":81,"outFeatures":970}},{"type":"update_params","name":"fc2","params":{"inFeatures":970,"outFeatures":970}},{"type":"update_params","name":"head","params":{"inFeatures":970,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":1040810}} {"id":"gen-mlp-1310","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 467-feature input with 161 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1310\nComponents:\n - input (input) params={\"shape\":[1,467]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,467]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":467,"outFeatures":749}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":749,"outFeatures":865}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":865,"outFeatures":161}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1136933}} {"id":"gen-ae-1311","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1420-dim input: encode down to a 133-dim bottleneck and decode back to 1420. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1311\nComponents:\n - input (input) params={\"shape\":[1,1420]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1420]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1420,"outFeatures":605}},{"componentType":"linear","name":"enc2","params":{"inFeatures":605,"outFeatures":133}},{"componentType":"linear","name":"dec1","params":{"inFeatures":133,"outFeatures":605}},{"componentType":"linear","name":"dec2","params":{"inFeatures":605,"outFeatures":1420}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1879130}} {"id":"gen-cnn-1312","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 71 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1312\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":57,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":39,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":75499}} {"id":"gen-txf-1313","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 468-token sequences with 48 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1313\nComponents:\n - input (input) params={\"shape\":[1,468]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,468]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48975,"embeddingDim":96}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":96,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4743072}} {"id":"gen-gqa-1314","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 228-token sequences with 40 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1314\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27452,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":10117056}} {"id":"gen-trim-1316","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2394-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1316\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":2394}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2394,\"outFeatures\":2394}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2394,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":309}},{"type":"update_params","name":"fc2","params":{"inFeatures":309,"outFeatures":309}},{"type":"update_params","name":"head","params":{"inFeatures":309,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":124527}} {"id":"gen-norm-1317","family":"norm","seed":20260716,"spec":"This 198-feature, 83-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1317\nComponents:\n - input (input) params={\"shape\":[1,198]}\n - fc1 (linear) params={\"inFeatures\":198,\"outFeatures\":119}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":119,\"outFeatures\":253}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":253,\"outFeatures\":171}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":171,\"outFeatures\":83}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":119}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":253}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":171}}],"grade":{"pass":true,"score":100,"params":111125}} {"id":"gen-tower-1318","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 114-feature user input and a 252-feature item input, each through its own 2-layer MLP tower ending at width 164, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1318\nComponents:\n - user_input (input) params={\"shape\":[1,114]}\n - item_input (input) params={\"shape\":[1,252]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,114]}},{"componentType":"input","name":"item_input","params":{"shape":[1,252]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":114,"outFeatures":164}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":164,"outFeatures":164}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":252,"outFeatures":164}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":164,"outFeatures":164}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":328,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":114144}} {"id":"gen-grow-1319","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (36-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1319\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - fc1 (linear) params={\"inFeatures\":36,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":36,"outFeatures":1351}},{"type":"update_params","name":"fc2","params":{"inFeatures":1351,"outFeatures":1351}},{"type":"update_params","name":"head","params":{"inFeatures":1351,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":1918420}} {"id":"gen-mlp-1320","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 15-feature input with 148 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1320\nComponents:\n - input (input) params={\"shape\":[1,15]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,15]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":15,"outFeatures":293}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":293,"outFeatures":605}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":605,"outFeatures":890}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":890,"outFeatures":114}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":114,"outFeatures":148}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":838442}} {"id":"gen-ae-1321","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1271-dim input: encode down to a 77-dim bottleneck and decode back to 1271. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1321\nComponents:\n - input (input) params={\"shape\":[1,1271]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1271]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1271,"outFeatures":940}},{"componentType":"linear","name":"enc2","params":{"inFeatures":940,"outFeatures":77}},{"componentType":"linear","name":"dec1","params":{"inFeatures":77,"outFeatures":940}},{"componentType":"linear","name":"dec2","params":{"inFeatures":940,"outFeatures":1271}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2534240}} {"id":"gen-cnn-1322","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 8 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1322\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":13,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":14958}} {"id":"gen-txf-1323","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 253-token sequences with 9 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1323\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,253]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27297,"embeddingDim":448}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":448,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":448,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":448,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":448,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":14641536}} {"id":"gen-gqa-1324","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 326-token sequences with 50 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1324\nComponents:\n - input (input) params={\"shape\":[1,326]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,326]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39942,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":384,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":15356928}} {"id":"gen-fix-1325","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (438) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1325\nComponents:\n - input (input) params={\"shape\":[1,447]}\n - embed (embedding) params={\"numEmbeddings\":14991,\"embeddingDim\":438}\n - attn (multiHeadAttention) params={\"embedDim\":438,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":438,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":7338690}} {"id":"gen-trim-1326","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4048-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1326\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":4048}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4048,\"outFeatures\":4048}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4048,\"outFeatures\":4048}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4048,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":217,"outFeatures":234}},{"type":"update_params","name":"fc2","params":{"inFeatures":234,"outFeatures":234}},{"type":"update_params","name":"fc3","params":{"inFeatures":234,"outFeatures":234}},{"type":"update_params","name":"head","params":{"inFeatures":234,"outFeatures":40}}],"grade":{"pass":true,"score":84,"params":169650}} {"id":"gen-norm-1327","family":"norm","seed":20260716,"spec":"This 173-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1327\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - fc1 (linear) params={\"inFeatures\":173,\"outFeatures\":327}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":327,\"outFeatures\":317}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":317,\"outFeatures\":235}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":235,\"outFeatures\":39}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":39,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":327}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":317}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":235}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":39}}],"grade":{"pass":true,"score":100,"params":246503}} {"id":"gen-tower-1328","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 201-feature user input and a 46-feature item input, each through its own 2-layer MLP tower ending at width 466, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1328\nComponents:\n - user_input (input) params={\"shape\":[1,201]}\n - item_input (input) params={\"shape\":[1,46]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,201]}},{"componentType":"input","name":"item_input","params":{"shape":[1,46]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":201,"outFeatures":466}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":46,"outFeatures":466}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":932,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":550346}} {"id":"gen-grow-1329","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (72-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1329\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":72,"outFeatures":899}},{"type":"update_params","name":"fc2","params":{"inFeatures":899,"outFeatures":899}},{"type":"update_params","name":"head","params":{"inFeatures":899,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":890909}} {"id":"gen-mlp-1330","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 96-feature input with 187 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1330\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,96]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":96,"outFeatures":114}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":114,"outFeatures":1017}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1017,"outFeatures":187}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":317061}} {"id":"gen-ae-1331","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1323-dim input: encode down to a 159-dim bottleneck and decode back to 1323. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1331\nComponents:\n - input (input) params={\"shape\":[1,1323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1323]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1323,"outFeatures":888}},{"componentType":"linear","name":"enc2","params":{"inFeatures":888,"outFeatures":159}},{"componentType":"linear","name":"dec1","params":{"inFeatures":159,"outFeatures":888}},{"componentType":"linear","name":"dec2","params":{"inFeatures":888,"outFeatures":1323}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2632032}} {"id":"gen-cnn-1332","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 83 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1332\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":20,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":26454}} {"id":"gen-txf-1333","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 348-token sequences with 51 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1333\nComponents:\n - input (input) params={\"shape\":[1,348]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,348]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25177,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4564032}} {"id":"gen-gqa-1334","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 470-token sequences with 74 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1334\nComponents:\n - input (input) params={\"shape\":[1,470]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,470]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24247,"embeddingDim":720}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":720,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[720]}},{"componentType":"linear","name":"head","params":{"inFeatures":720,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":17511120}} {"id":"gen-trim-1336","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2529-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1336\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":2529}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2529,\"outFeatures\":2529}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2529,\"outFeatures\":2529}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2529,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":224,"outFeatures":115}},{"type":"update_params","name":"fc2","params":{"inFeatures":115,"outFeatures":115}},{"type":"update_params","name":"fc3","params":{"inFeatures":115,"outFeatures":115}},{"type":"update_params","name":"head","params":{"inFeatures":115,"outFeatures":37}}],"grade":{"pass":true,"score":84,"params":56465}} {"id":"gen-norm-1337","family":"norm","seed":20260716,"spec":"This 232-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1337\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - fc1 (linear) params={\"inFeatures\":232,\"outFeatures\":357}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":357,\"outFeatures\":190}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":190,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":357}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":190}}],"grade":{"pass":true,"score":94,"params":167184}} {"id":"gen-tower-1338","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 139-feature user input and a 183-feature item input, each through its own 2-layer MLP tower ending at width 129, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1338\nComponents:\n - user_input (input) params={\"shape\":[1,139]}\n - item_input (input) params={\"shape\":[1,183]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,139]}},{"componentType":"input","name":"item_input","params":{"shape":[1,183]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":139,"outFeatures":129}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":129,"outFeatures":129}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":183,"outFeatures":129}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":129,"outFeatures":129}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":258,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":75078}} {"id":"gen-grow-1339","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (52-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1339\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - fc1 (linear) params={\"inFeatures\":52,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":52,"outFeatures":1296}},{"type":"update_params","name":"fc2","params":{"inFeatures":1296,"outFeatures":1296}},{"type":"update_params","name":"head","params":{"inFeatures":1296,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":1792368}} {"id":"gen-mlp-1340","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 256-feature input with 162 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1340\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,256]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":256,"outFeatures":588}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":588,"outFeatures":421}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":421,"outFeatures":775}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":775,"outFeatures":317}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":317,"outFeatures":789}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":789,"outFeatures":162}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1347957}} {"id":"gen-ae-1341","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 575-dim input: encode down to a 246-dim bottleneck and decode back to 575. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1341\nComponents:\n - input (input) params={\"shape\":[1,575]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,575]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":575,"outFeatures":326}},{"componentType":"linear","name":"enc2","params":{"inFeatures":326,"outFeatures":246}},{"componentType":"linear","name":"dec1","params":{"inFeatures":246,"outFeatures":326}},{"componentType":"linear","name":"dec2","params":{"inFeatures":326,"outFeatures":575}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":535292}} {"id":"gen-cnn-1342","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 81 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1342\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":27,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":24,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":30,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":21816}} {"id":"gen-txf-1343","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 227-token sequences with 16 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1343\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43565,"embeddingDim":34}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":34,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1486378}} {"id":"gen-gqa-1344","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 483-token sequences with 39 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1344\nComponents:\n - input (input) params={\"shape\":[1,483]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,483]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23955,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2495376}} {"id":"gen-trim-1346","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3010-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1346\nComponents:\n - input (input) params={\"shape\":[1,131]}\n - fc1 (linear) params={\"inFeatures\":131,\"outFeatures\":3010}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3010,\"outFeatures\":3010}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3010,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":131,"outFeatures":109}},{"type":"update_params","name":"fc2","params":{"inFeatures":109,"outFeatures":109}},{"type":"update_params","name":"head","params":{"inFeatures":109,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":30193}} {"id":"gen-norm-1347","family":"norm","seed":20260716,"spec":"This 71-feature, 46-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1347\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - fc1 (linear) params={\"inFeatures\":71,\"outFeatures\":435}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":435,\"outFeatures\":179}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":179,\"outFeatures\":421}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":421,\"outFeatures\":276}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":276,\"outFeatures\":95}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":95,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":435}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":179}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":421}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":95}}],"grade":{"pass":true,"score":100,"params":330895}} {"id":"gen-tower-1348","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 142-feature user input and a 178-feature item input, each through its own 2-layer MLP tower ending at width 67, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1348\nComponents:\n - user_input (input) params={\"shape\":[1,142]}\n - item_input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,142]}},{"componentType":"input","name":"item_input","params":{"shape":[1,178]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":142,"outFeatures":67}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":67,"outFeatures":67}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":178,"outFeatures":67}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":67,"outFeatures":67}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":134,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30552}} {"id":"gen-grow-1349","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (48-feature input, 13 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1349\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":48,"outFeatures":914}},{"type":"update_params","name":"fc2","params":{"inFeatures":914,"outFeatures":914}},{"type":"update_params","name":"head","params":{"inFeatures":914,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":891150}} {"id":"gen-mlp-1350","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 482-feature input with 193 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1350\nComponents:\n - input (input) params={\"shape\":[1,482]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,482]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":482,"outFeatures":174}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":174,"outFeatures":816}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":816,"outFeatures":648}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":648,"outFeatures":968}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":968,"outFeatures":193}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1568708}} {"id":"gen-ae-1351","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 683-dim input: encode down to a 61-dim bottleneck and decode back to 683. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1351\nComponents:\n - input (input) params={\"shape\":[1,683]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,683]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":683,"outFeatures":869}},{"componentType":"linear","name":"enc2","params":{"inFeatures":869,"outFeatures":61}},{"componentType":"linear","name":"dec1","params":{"inFeatures":61,"outFeatures":869}},{"componentType":"linear","name":"dec2","params":{"inFeatures":869,"outFeatures":683}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1293072}} {"id":"gen-cnn-1352","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 11 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1352\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":63,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":48886}} {"id":"gen-txf-1353","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 477-token sequences with 87 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1353\nComponents:\n - input (input) params={\"shape\":[1,477]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,477]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28019,"embeddingDim":216}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":216,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6257520}} {"id":"gen-gqa-1354","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 493-token sequences with 51 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1354\nComponents:\n - input (input) params={\"shape\":[1,493]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,493]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10371,"embeddingDim":376}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":376,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[376]}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3918672}} {"id":"gen-trim-1356","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1777-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1356\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":1777}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1777,\"outFeatures\":1777}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1777,\"outFeatures\":1777}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1777,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":224,"outFeatures":332}},{"type":"update_params","name":"fc2","params":{"inFeatures":332,"outFeatures":332}},{"type":"update_params","name":"fc3","params":{"inFeatures":332,"outFeatures":332}},{"type":"update_params","name":"head","params":{"inFeatures":332,"outFeatures":22}}],"grade":{"pass":true,"score":84,"params":302120}} {"id":"gen-norm-1357","family":"norm","seed":20260716,"spec":"This 26-feature, 75-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1357\nComponents:\n - input (input) params={\"shape\":[1,26]}\n - fc1 (linear) params={\"inFeatures\":26,\"outFeatures\":206}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":206,\"outFeatures\":69}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":69,\"outFeatures\":306}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":306,\"outFeatures\":52}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":52,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":206}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":69}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":306}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":52}}],"grade":{"pass":true,"score":100,"params":60496}} {"id":"gen-tower-1358","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 157-feature user input and a 190-feature item input, each through its own 2-layer MLP tower ending at width 367, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1358\nComponents:\n - user_input (input) params={\"shape\":[1,157]}\n - item_input (input) params={\"shape\":[1,190]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,157]}},{"componentType":"input","name":"item_input","params":{"shape":[1,190]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":157,"outFeatures":367}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":367,"outFeatures":367}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":190,"outFeatures":367}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":367,"outFeatures":367}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":734,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":397461}} {"id":"gen-grow-1359","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (20-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1359\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":20,"outFeatures":1311}},{"type":"update_params","name":"fc2","params":{"inFeatures":1311,"outFeatures":1311}},{"type":"update_params","name":"head","params":{"inFeatures":1311,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":1779027}} {"id":"gen-mlp-1360","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 206-feature input with 52 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1360\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,206]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":206,"outFeatures":777}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":777,"outFeatures":767}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":767,"outFeatures":424}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":424,"outFeatures":692}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":692,"outFeatures":446}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":446,"outFeatures":509}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":509,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1936751}} {"id":"gen-ae-1361","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1888-dim input: encode down to a 184-dim bottleneck and decode back to 1888. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1361\nComponents:\n - input (input) params={\"shape\":[1,1888]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1888]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1888,"outFeatures":211}},{"componentType":"linear","name":"enc2","params":{"inFeatures":211,"outFeatures":184}},{"componentType":"linear","name":"dec1","params":{"inFeatures":184,"outFeatures":211}},{"componentType":"linear","name":"dec2","params":{"inFeatures":211,"outFeatures":1888}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":874384}} {"id":"gen-cnn-1362","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 86 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1362\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":18,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":27,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":23785}} {"id":"gen-txf-1363","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 88-token sequences with 17 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1363\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,88]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9678,"embeddingDim":456}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":456,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":456,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":6084408}} {"id":"gen-gqa-1364","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 495-token sequences with 99 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1364\nComponents:\n - input (input) params={\"shape\":[1,495]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,495]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31045,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3986432}} {"id":"gen-trim-1366","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2077-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1366\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":2077}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2077,\"outFeatures\":2077}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2077,\"outFeatures\":2077}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2077,\"outFeatures\":2077}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2077,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":310}},{"type":"update_params","name":"fc2","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"fc3","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"fc4","params":{"inFeatures":310,"outFeatures":310}},{"type":"update_params","name":"head","params":{"inFeatures":310,"outFeatures":32}}],"grade":{"pass":true,"score":88,"params":310000}} {"id":"gen-norm-1367","family":"norm","seed":20260716,"spec":"This 196-feature, 47-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1367\nComponents:\n - input (input) params={\"shape\":[1,196]}\n - fc1 (linear) params={\"inFeatures\":196,\"outFeatures\":118}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":118,\"outFeatures\":67}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":67,\"outFeatures\":149}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":149,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":118}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":67}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":149}}],"grade":{"pass":true,"score":100,"params":48020}} {"id":"gen-tower-1368","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 125-feature user input and a 248-feature item input, each through its own 2-layer MLP tower ending at width 291, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1368\nComponents:\n - user_input (input) params={\"shape\":[1,125]}\n - item_input (input) params={\"shape\":[1,248]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,125]}},{"componentType":"input","name":"item_input","params":{"shape":[1,248]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":125,"outFeatures":291}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":248,"outFeatures":291}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":582,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":278487}} {"id":"gen-grow-1369","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (45-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1369\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - fc1 (linear) params={\"inFeatures\":45,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":45,"outFeatures":1255}},{"type":"update_params","name":"fc2","params":{"inFeatures":1255,"outFeatures":1255}},{"type":"update_params","name":"head","params":{"inFeatures":1255,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1655345}} {"id":"gen-mlp-1370","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 321-feature input with 168 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1370\nComponents:\n - input (input) params={\"shape\":[1,321]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,321]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":321,"outFeatures":91}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":91,"outFeatures":406}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":406,"outFeatures":168}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":134365}} {"id":"gen-ae-1371","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1084-dim input: encode down to a 82-dim bottleneck and decode back to 1084. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1371\nComponents:\n - input (input) params={\"shape\":[1,1084]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1084]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1084,"outFeatures":870}},{"componentType":"linear","name":"enc2","params":{"inFeatures":870,"outFeatures":82}},{"componentType":"linear","name":"dec1","params":{"inFeatures":82,"outFeatures":870}},{"componentType":"linear","name":"dec2","params":{"inFeatures":870,"outFeatures":1084}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2028840}} {"id":"gen-cnn-1372","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 26 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1372\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4342}} {"id":"gen-txf-1373","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 386-token sequences with 72 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1373\nComponents:\n - input (input) params={\"shape\":[1,386]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,386]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37131,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3001840}} {"id":"gen-gqa-1374","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 418-token sequences with 80 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1374\nComponents:\n - input (input) params={\"shape\":[1,418]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,418]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30532,"embeddingDim":320}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":320,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[320]}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":9795840}} {"id":"gen-fix-1375","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (332) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1375\nComponents:\n - input (input) params={\"shape\":[1,213]}\n - embed (embedding) params={\"numEmbeddings\":41534,\"embeddingDim\":332}\n - attn (multiHeadAttention) params={\"embedDim\":332,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":332,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":14248112}} {"id":"gen-trim-1376","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3118-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1376\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - fc1 (linear) params={\"inFeatures\":233,\"outFeatures\":3118}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3118,\"outFeatures\":3118}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3118,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":233,"outFeatures":372}},{"type":"update_params","name":"fc2","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"head","params":{"inFeatures":372,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":236592}} {"id":"gen-norm-1377","family":"norm","seed":20260716,"spec":"This 182-feature, 11-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1377\nComponents:\n - input (input) params={\"shape\":[1,182]}\n - fc1 (linear) params={\"inFeatures\":182,\"outFeatures\":358}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":358,\"outFeatures\":377}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":377,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":358}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":377}}],"grade":{"pass":true,"score":94,"params":204269}} {"id":"gen-tower-1378","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 187-feature user input and a 103-feature item input, each through its own 2-layer MLP tower ending at width 82, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1378\nComponents:\n - user_input (input) params={\"shape\":[1,187]}\n - item_input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,187]}},{"componentType":"input","name":"item_input","params":{"shape":[1,103]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":187,"outFeatures":82}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":103,"outFeatures":82}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":164,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37392}} {"id":"gen-grow-1379","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (108-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1379\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":1361}},{"type":"update_params","name":"fc2","params":{"inFeatures":1361,"outFeatures":1361}},{"type":"update_params","name":"head","params":{"inFeatures":1361,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":2052388}} {"id":"gen-mlp-1380","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 233-feature input with 3 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1380\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,233]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":233,"outFeatures":915}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":915,"outFeatures":91}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":91,"outFeatures":735}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":735,"outFeatures":411}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":411,"outFeatures":541}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":541,"outFeatures":103}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":103,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":943813}} {"id":"gen-ae-1381","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 281-dim input: encode down to a 169-dim bottleneck and decode back to 281. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1381\nComponents:\n - input (input) params={\"shape\":[1,281]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,281]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":281,"outFeatures":369}},{"componentType":"linear","name":"enc2","params":{"inFeatures":369,"outFeatures":169}},{"componentType":"linear","name":"dec1","params":{"inFeatures":169,"outFeatures":369}},{"componentType":"linear","name":"dec2","params":{"inFeatures":369,"outFeatures":281}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":332100}} {"id":"gen-cnn-1382","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 41 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1382\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":45,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30202}} {"id":"gen-txf-1383","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 62-token sequences with 17 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1383\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,62]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49114,"embeddingDim":86}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":86,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":86,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":86,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":86,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4314018}} {"id":"gen-gqa-1384","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 331-token sequences with 82 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1384\nComponents:\n - input (input) params={\"shape\":[1,331]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,331]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30355,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":368,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":368,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11200816}} {"id":"gen-trim-1386","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1942-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1386\nComponents:\n - input (input) params={\"shape\":[1,131]}\n - fc1 (linear) params={\"inFeatures\":131,\"outFeatures\":1942}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1942,\"outFeatures\":1942}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1942,\"outFeatures\":1942}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1942,\"outFeatures\":1942}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1942,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":131,"outFeatures":266}},{"type":"update_params","name":"fc2","params":{"inFeatures":266,"outFeatures":266}},{"type":"update_params","name":"fc3","params":{"inFeatures":266,"outFeatures":266}},{"type":"update_params","name":"fc4","params":{"inFeatures":266,"outFeatures":266}},{"type":"update_params","name":"head","params":{"inFeatures":266,"outFeatures":2}}],"grade":{"pass":true,"score":88,"params":247646}} {"id":"gen-norm-1387","family":"norm","seed":20260716,"spec":"This 220-feature, 14-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1387\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":60}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":60,\"outFeatures\":440}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":440,\"outFeatures\":228}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":228,\"outFeatures\":383}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":383,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":60}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":440}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":228}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":383}}],"grade":{"pass":true,"score":100,"params":232606}} {"id":"gen-tower-1388","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 186-feature user input and a 52-feature item input, each through its own 2-layer MLP tower ending at width 192, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1388\nComponents:\n - user_input (input) params={\"shape\":[1,186]}\n - item_input (input) params={\"shape\":[1,52]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,186]}},{"componentType":"input","name":"item_input","params":{"shape":[1,52]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":186,"outFeatures":192}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":192,"outFeatures":192}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":52,"outFeatures":192}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":192,"outFeatures":192}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":119808}} {"id":"gen-grow-1389","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (59-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1389\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":59,"outFeatures":1400}},{"type":"update_params","name":"fc2","params":{"inFeatures":1400,"outFeatures":1400}},{"type":"update_params","name":"head","params":{"inFeatures":1400,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":2083200}} {"id":"gen-mlp-1390","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 504-feature input with 125 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1390\nComponents:\n - input (input) params={\"shape\":[1,504]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,504]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":504,"outFeatures":450}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":450,"outFeatures":274}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":274,"outFeatures":753}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":753,"outFeatures":667}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":667,"outFeatures":868}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":868,"outFeatures":168}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":168,"outFeatures":125}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1804453}} {"id":"gen-ae-1391","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1185-dim input: encode down to a 11-dim bottleneck and decode back to 1185. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1391\nComponents:\n - input (input) params={\"shape\":[1,1185]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1185]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1185,"outFeatures":816}},{"componentType":"linear","name":"enc2","params":{"inFeatures":816,"outFeatures":11}},{"componentType":"linear","name":"dec1","params":{"inFeatures":11,"outFeatures":816}},{"componentType":"linear","name":"dec2","params":{"inFeatures":816,"outFeatures":1185}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1951872}} {"id":"gen-cnn-1392","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x62x62 image with 18 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1392\nComponents:\n - input (input) params={\"shape\":[1,3,62,62]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,62,62]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":48,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":29196}} {"id":"gen-txf-1393","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 111-token sequences with 57 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1393\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,111]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27534,"embeddingDim":66}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":66,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":66,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":66,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":66,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1873278}} {"id":"gen-gqa-1394","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 397-token sequences with 59 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1394\nComponents:\n - input (input) params={\"shape\":[1,397]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,397]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12354,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3773552}} {"id":"gen-trim-1396","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3539-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1396\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":3539}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3539,\"outFeatures\":3539}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3539,\"outFeatures\":3539}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3539,\"outFeatures\":3539}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3539,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":206,"outFeatures":335}},{"type":"update_params","name":"fc2","params":{"inFeatures":335,"outFeatures":335}},{"type":"update_params","name":"fc3","params":{"inFeatures":335,"outFeatures":335}},{"type":"update_params","name":"fc4","params":{"inFeatures":335,"outFeatures":335}},{"type":"update_params","name":"head","params":{"inFeatures":335,"outFeatures":49}}],"grade":{"pass":true,"score":88,"params":422100}} {"id":"gen-norm-1397","family":"norm","seed":20260716,"spec":"This 233-feature, 63-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1397\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - fc1 (linear) params={\"inFeatures\":233,\"outFeatures\":503}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":503,\"outFeatures\":348}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":348,\"outFeatures\":386}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":386,\"outFeatures\":63}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":503}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":348}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":386}}],"grade":{"pass":true,"score":100,"params":450889}} {"id":"gen-tower-1398","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 143-feature user input and a 129-feature item input, each through its own 2-layer MLP tower ending at width 174, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1398\nComponents:\n - user_input (input) params={\"shape\":[1,143]}\n - item_input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,143]}},{"componentType":"input","name":"item_input","params":{"shape":[1,129]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":143,"outFeatures":174}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":174,"outFeatures":174}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":129,"outFeatures":174}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":174,"outFeatures":174}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":348,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":108228}} {"id":"gen-grow-1399","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (49-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1399\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":49,"outFeatures":787}},{"type":"update_params","name":"fc2","params":{"inFeatures":787,"outFeatures":787}},{"type":"update_params","name":"head","params":{"inFeatures":787,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":679968}} {"id":"gen-mlp-1400","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 242-feature input with 12 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1400\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,242]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":242,"outFeatures":996}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":996,"outFeatures":713}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":713,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":959736}} {"id":"gen-ae-1401","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1443-dim input: encode down to a 94-dim bottleneck and decode back to 1443. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1401\nComponents:\n - input (input) params={\"shape\":[1,1443]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1443]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1443,"outFeatures":574}},{"componentType":"linear","name":"enc2","params":{"inFeatures":574,"outFeatures":94}},{"componentType":"linear","name":"dec1","params":{"inFeatures":94,"outFeatures":574}},{"componentType":"linear","name":"dec2","params":{"inFeatures":574,"outFeatures":1443}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1764476}} {"id":"gen-cnn-1402","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 28 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1402\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":5114}} {"id":"gen-txf-1403","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 134-token sequences with 14 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1403\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,134]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45548,"embeddingDim":148}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":148,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":148,"outFeatures":14}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6830792}} {"id":"gen-gqa-1404","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 104-token sequences with 28 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1404\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,104]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24454,"embeddingDim":592}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":592,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[592]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":592,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[592]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":592,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[592]}},{"componentType":"linear","name":"head","params":{"inFeatures":592,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":14493344}} {"id":"gen-trim-1406","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3247-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1406\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - fc1 (linear) params={\"inFeatures\":134,\"outFeatures\":3247}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3247,\"outFeatures\":3247}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3247,\"outFeatures\":3247}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3247,\"outFeatures\":3247}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3247,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":134,"outFeatures":269}},{"type":"update_params","name":"fc2","params":{"inFeatures":269,"outFeatures":269}},{"type":"update_params","name":"fc3","params":{"inFeatures":269,"outFeatures":269}},{"type":"update_params","name":"fc4","params":{"inFeatures":269,"outFeatures":269}},{"type":"update_params","name":"head","params":{"inFeatures":269,"outFeatures":8}}],"grade":{"pass":true,"score":88,"params":255281}} {"id":"gen-norm-1407","family":"norm","seed":20260716,"spec":"This 106-feature, 69-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1407\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":507}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":507,\"outFeatures\":462}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":462,\"outFeatures\":69}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":507}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":462}}],"grade":{"pass":true,"score":94,"params":319854}} {"id":"gen-tower-1408","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 98-feature user input and a 20-feature item input, each through its own 2-layer MLP tower ending at width 128, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1408\nComponents:\n - user_input (input) params={\"shape\":[1,98]}\n - item_input (input) params={\"shape\":[1,20]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,98]}},{"componentType":"input","name":"item_input","params":{"shape":[1,20]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":98,"outFeatures":128}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":128,"outFeatures":128}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":20,"outFeatures":128}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":128,"outFeatures":128}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":48128}} {"id":"gen-grow-1409","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1409\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":739}},{"type":"update_params","name":"fc2","params":{"inFeatures":739,"outFeatures":739}},{"type":"update_params","name":"head","params":{"inFeatures":739,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":591200}} {"id":"gen-mlp-1410","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 173-feature input with 144 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1410\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,173]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":173,"outFeatures":242}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":242,"outFeatures":729}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":729,"outFeatures":438}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":438,"outFeatures":779}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":779,"outFeatures":117}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":117,"outFeatures":144}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":986779}} {"id":"gen-ae-1411","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2007-dim input: encode down to a 53-dim bottleneck and decode back to 2007. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1411\nComponents:\n - input (input) params={\"shape\":[1,2007]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2007]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2007,"outFeatures":1011}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1011,"outFeatures":53}},{"componentType":"linear","name":"dec1","params":{"inFeatures":53,"outFeatures":1011}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1011,"outFeatures":2007}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4165320}} {"id":"gen-cnn-1412","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 18 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1412\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":45369}} {"id":"gen-txf-1413","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 319-token sequences with 49 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1413\nComponents:\n - input (input) params={\"shape\":[1,319]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,319]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31381,"embeddingDim":98}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":98,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":98,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3118556}} {"id":"gen-gqa-1414","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 494-token sequences with 29 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1414\nComponents:\n - input (input) params={\"shape\":[1,494]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,494]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9741,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":1797680}} {"id":"gen-trim-1416","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2978-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1416\nComponents:\n - input (input) params={\"shape\":[1,246]}\n - fc1 (linear) params={\"inFeatures\":246,\"outFeatures\":2978}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2978,\"outFeatures\":2978}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2978,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":246,"outFeatures":207}},{"type":"update_params","name":"fc2","params":{"inFeatures":207,"outFeatures":207}},{"type":"update_params","name":"head","params":{"inFeatures":207,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":101430}} {"id":"gen-norm-1417","family":"norm","seed":20260716,"spec":"This 27-feature, 3-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1417\nComponents:\n - input (input) params={\"shape\":[1,27]}\n - fc1 (linear) params={\"inFeatures\":27,\"outFeatures\":337}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":337,\"outFeatures\":263}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":263,\"outFeatures\":63}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":63,\"outFeatures\":477}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":477,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":337}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":263}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":63}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":477}}],"grade":{"pass":true,"score":100,"params":145781}} {"id":"gen-tower-1418","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 225-feature user input and a 162-feature item input, each through its own 2-layer MLP tower ending at width 364, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1418\nComponents:\n - user_input (input) params={\"shape\":[1,225]}\n - item_input (input) params={\"shape\":[1,162]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,225]}},{"componentType":"input","name":"item_input","params":{"shape":[1,162]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":225,"outFeatures":364}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":364,"outFeatures":364}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":162,"outFeatures":364}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":364,"outFeatures":364}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":728,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":406588}} {"id":"gen-grow-1419","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (118-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1419\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":118,"outFeatures":1289}},{"type":"update_params","name":"fc2","params":{"inFeatures":1289,"outFeatures":1289}},{"type":"update_params","name":"head","params":{"inFeatures":1289,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":1817490}} {"id":"gen-mlp-1420","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 192-feature input with 169 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1420\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,192]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":192,"outFeatures":341}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":341,"outFeatures":438}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":438,"outFeatures":749}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":749,"outFeatures":225}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":225,"outFeatures":640}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":640,"outFeatures":169}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":963577}} {"id":"gen-ae-1421","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1441-dim input: encode down to a 50-dim bottleneck and decode back to 1441. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1421\nComponents:\n - input (input) params={\"shape\":[1,1441]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1441]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1441,"outFeatures":462}},{"componentType":"linear","name":"enc2","params":{"inFeatures":462,"outFeatures":50}},{"componentType":"linear","name":"dec1","params":{"inFeatures":50,"outFeatures":462}},{"componentType":"linear","name":"dec2","params":{"inFeatures":462,"outFeatures":1441}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1377684}} {"id":"gen-cnn-1422","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 77 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1422\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12828}} {"id":"gen-txf-1423","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 451-token sequences with 43 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1423\nComponents:\n - input (input) params={\"shape\":[1,451]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,451]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24055,"embeddingDim":64}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":64,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":64,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1575040}} {"id":"gen-gqa-1424","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 495-token sequences with 59 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1424\nComponents:\n - input (input) params={\"shape\":[1,495]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,495]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37850,"embeddingDim":76}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":76,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[76]}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2881084}} {"id":"gen-fix-1425","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (494) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1425\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - embed (embedding) params={\"numEmbeddings\":10055,\"embeddingDim\":494}\n - attn (multiHeadAttention) params={\"embedDim\":494,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":494,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5947760}} {"id":"gen-trim-1426","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3533-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1426\nComponents:\n - input (input) params={\"shape\":[1,184]}\n - fc1 (linear) params={\"inFeatures\":184,\"outFeatures\":3533}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3533,\"outFeatures\":3533}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3533,\"outFeatures\":3533}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3533,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":184,"outFeatures":134}},{"type":"update_params","name":"fc2","params":{"inFeatures":134,"outFeatures":134}},{"type":"update_params","name":"fc3","params":{"inFeatures":134,"outFeatures":134}},{"type":"update_params","name":"head","params":{"inFeatures":134,"outFeatures":36}}],"grade":{"pass":true,"score":84,"params":65392}} {"id":"gen-norm-1427","family":"norm","seed":20260716,"spec":"This 127-feature, 9-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1427\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":174}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":174,\"outFeatures\":78}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":78,\"outFeatures\":149}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":149,\"outFeatures\":493}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":493,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":174}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":78}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":149}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":493}}],"grade":{"pass":true,"score":100,"params":125186}} {"id":"gen-tower-1428","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 117-feature user input and a 229-feature item input, each through its own 2-layer MLP tower ending at width 302, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1428\nComponents:\n - user_input (input) params={\"shape\":[1,117]}\n - item_input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,117]}},{"componentType":"input","name":"item_input","params":{"shape":[1,229]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":117,"outFeatures":302}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":302,"outFeatures":302}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":229,"outFeatures":302}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":302,"outFeatures":302}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":604,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":287504}} {"id":"gen-grow-1429","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (85-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1429\nComponents:\n - input (input) params={\"shape\":[1,85]}\n - fc1 (linear) params={\"inFeatures\":85,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":85,"outFeatures":1004}},{"type":"update_params","name":"fc2","params":{"inFeatures":1004,"outFeatures":1004}},{"type":"update_params","name":"head","params":{"inFeatures":1004,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":1107412}} {"id":"gen-mlp-1430","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 147-feature input with 129 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1430\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,147]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":147,"outFeatures":516}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":516,"outFeatures":129}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":129,"outFeatures":275}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":275,"outFeatures":652}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":652,"outFeatures":996}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":996,"outFeatures":826}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":826,"outFeatures":129}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1935833}} {"id":"gen-ae-1431","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 64-dim input: encode down to a 153-dim bottleneck and decode back to 64. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1431\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,64]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":64,"outFeatures":641}},{"componentType":"linear","name":"enc2","params":{"inFeatures":641,"outFeatures":153}},{"componentType":"linear","name":"dec1","params":{"inFeatures":153,"outFeatures":641}},{"componentType":"linear","name":"dec2","params":{"inFeatures":641,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":278194}} {"id":"gen-cnn-1432","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 83 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1432\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":15,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":46065}} {"id":"gen-txf-1433","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 482-token sequences with 43 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1433\nComponents:\n - input (input) params={\"shape\":[1,482]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,482]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25656,"embeddingDim":208}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":208,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":208,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":208,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5864560}} {"id":"gen-gqa-1434","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 333-token sequences with 70 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1434\nComponents:\n - input (input) params={\"shape\":[1,333]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,333]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15273,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":96,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":96,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1472928}} {"id":"gen-fix-1435","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (284) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1435\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - embed (embedding) params={\"numEmbeddings\":44636,\"embeddingDim\":284}\n - attn (multiHeadAttention) params={\"embedDim\":284,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":284,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":13003792}} {"id":"gen-trim-1436","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2285-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1436\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - fc1 (linear) params={\"inFeatures\":123,\"outFeatures\":2285}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2285,\"outFeatures\":2285}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2285,\"outFeatures\":2285}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2285,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":123,"outFeatures":382}},{"type":"update_params","name":"fc2","params":{"inFeatures":382,"outFeatures":382}},{"type":"update_params","name":"fc3","params":{"inFeatures":382,"outFeatures":382}},{"type":"update_params","name":"head","params":{"inFeatures":382,"outFeatures":29}}],"grade":{"pass":true,"score":84,"params":349912}} {"id":"gen-norm-1437","family":"norm","seed":20260716,"spec":"This 137-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1437\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - fc1 (linear) params={\"inFeatures\":137,\"outFeatures\":47}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":47,\"outFeatures\":300}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":300,\"outFeatures\":501}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":501,\"outFeatures\":441}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":441,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":47}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":300}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":501}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":441}}],"grade":{"pass":true,"score":100,"params":405010}} {"id":"gen-tower-1438","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 140-feature user input and a 125-feature item input, each through its own 2-layer MLP tower ending at width 271, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1438\nComponents:\n - user_input (input) params={\"shape\":[1,140]}\n - item_input (input) params={\"shape\":[1,125]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,140]}},{"componentType":"input","name":"item_input","params":{"shape":[1,125]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":140,"outFeatures":271}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":125,"outFeatures":271}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":542,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":219239}} {"id":"gen-grow-1439","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1439\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":1089}},{"type":"update_params","name":"fc2","params":{"inFeatures":1089,"outFeatures":1089}},{"type":"update_params","name":"head","params":{"inFeatures":1089,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":1313334}} {"id":"gen-mlp-1440","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 249-feature input with 77 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1440\nComponents:\n - input (input) params={\"shape\":[1,249]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,249]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":249,"outFeatures":868}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":868,"outFeatures":187}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":187,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":392847}} {"id":"gen-ae-1441","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1790-dim input: encode down to a 117-dim bottleneck and decode back to 1790. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1441\nComponents:\n - input (input) params={\"shape\":[1,1790]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1790]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1790,"outFeatures":408}},{"componentType":"linear","name":"enc2","params":{"inFeatures":408,"outFeatures":117}},{"componentType":"linear","name":"dec1","params":{"inFeatures":117,"outFeatures":408}},{"componentType":"linear","name":"dec2","params":{"inFeatures":408,"outFeatures":1790}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1556112}} {"id":"gen-cnn-1442","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 20 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1442\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":45,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36325}} {"id":"gen-txf-1443","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 269-token sequences with 26 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1443\nComponents:\n - input (input) params={\"shape\":[1,269]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,269]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25956,"embeddingDim":100}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":100,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2638200}} {"id":"gen-gqa-1444","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 59-token sequences with 12 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1444\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,59]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19989,"embeddingDim":48}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":48,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[48]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":48,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[48]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":48,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[48]}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":960048}} {"id":"gen-fix-1445","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (88) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1445\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - embed (embedding) params={\"numEmbeddings\":25132,\"embeddingDim\":88}\n - attn (multiHeadAttention) params={\"embedDim\":88,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":88,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":2242944}} {"id":"gen-trim-1446","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2892-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1446\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":2892}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2892,\"outFeatures\":2892}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2892,\"outFeatures\":2892}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2892,\"outFeatures\":2892}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2892,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":107,"outFeatures":81}},{"type":"update_params","name":"fc2","params":{"inFeatures":81,"outFeatures":81}},{"type":"update_params","name":"fc3","params":{"inFeatures":81,"outFeatures":81}},{"type":"update_params","name":"fc4","params":{"inFeatures":81,"outFeatures":81}},{"type":"update_params","name":"head","params":{"inFeatures":81,"outFeatures":41}}],"grade":{"pass":true,"score":88,"params":31671}} {"id":"gen-norm-1447","family":"norm","seed":20260716,"spec":"This 61-feature, 22-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1447\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - fc1 (linear) params={\"inFeatures\":61,\"outFeatures\":388}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":388,\"outFeatures\":106}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":106,\"outFeatures\":124}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":124,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":388}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":106}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":124}}],"grade":{"pass":true,"score":100,"params":80668}} {"id":"gen-tower-1448","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 112-feature user input and a 242-feature item input, each through its own 2-layer MLP tower ending at width 273, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1448\nComponents:\n - user_input (input) params={\"shape\":[1,112]}\n - item_input (input) params={\"shape\":[1,242]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,112]}},{"componentType":"input","name":"item_input","params":{"shape":[1,242]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":112,"outFeatures":273}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":273,"outFeatures":273}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":242,"outFeatures":273}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":273,"outFeatures":273}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":546,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":246246}} {"id":"gen-grow-1449","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (127-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1449\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":127,"outFeatures":684}},{"type":"update_params","name":"fc2","params":{"inFeatures":684,"outFeatures":684}},{"type":"update_params","name":"head","params":{"inFeatures":684,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":578664}} {"id":"gen-mlp-1450","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 308-feature input with 19 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1450\nComponents:\n - input (input) params={\"shape\":[1,308]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,308]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":308,"outFeatures":986}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":986,"outFeatures":107}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":107,"outFeatures":76}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":76,"outFeatures":429}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":429,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":458077}} {"id":"gen-ae-1451","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 328-dim input: encode down to a 53-dim bottleneck and decode back to 328. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1451\nComponents:\n - input (input) params={\"shape\":[1,328]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,328]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":328,"outFeatures":814}},{"componentType":"linear","name":"enc2","params":{"inFeatures":814,"outFeatures":53}},{"componentType":"linear","name":"dec1","params":{"inFeatures":53,"outFeatures":814}},{"componentType":"linear","name":"dec2","params":{"inFeatures":814,"outFeatures":328}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":620268}} {"id":"gen-cnn-1452","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 44 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1452\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":63,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":28314}} {"id":"gen-txf-1453","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 419-token sequences with 43 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1453\nComponents:\n - input (input) params={\"shape\":[1,419]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,419]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8664,"embeddingDim":50}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":50,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":50,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":445350}} {"id":"gen-gqa-1454","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 423-token sequences with 77 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1454\nComponents:\n - input (input) params={\"shape\":[1,423]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,423]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42348,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":184,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7806200}} {"id":"gen-trim-1456","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3294-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1456\nComponents:\n - input (input) params={\"shape\":[1,241]}\n - fc1 (linear) params={\"inFeatures\":241,\"outFeatures\":3294}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3294,\"outFeatures\":3294}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3294,\"outFeatures\":3294}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3294,\"outFeatures\":3294}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3294,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":241,"outFeatures":203}},{"type":"update_params","name":"fc2","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"fc3","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"fc4","params":{"inFeatures":203,"outFeatures":203}},{"type":"update_params","name":"head","params":{"inFeatures":203,"outFeatures":50}}],"grade":{"pass":true,"score":88,"params":182700}} {"id":"gen-norm-1457","family":"norm","seed":20260716,"spec":"This 116-feature, 66-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1457\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":503}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":503,\"outFeatures\":214}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":214,\"outFeatures\":66}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":503}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":214}}],"grade":{"pass":true,"score":94,"params":180114}} {"id":"gen-tower-1458","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 154-feature user input and a 231-feature item input, each through its own 2-layer MLP tower ending at width 71, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1458\nComponents:\n - user_input (input) params={\"shape\":[1,154]}\n - item_input (input) params={\"shape\":[1,231]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,154]}},{"componentType":"input","name":"item_input","params":{"shape":[1,231]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":154,"outFeatures":71}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":71,"outFeatures":71}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":231,"outFeatures":71}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":71,"outFeatures":71}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":142,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37559}} {"id":"gen-grow-1459","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (29-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1459\nComponents:\n - input (input) params={\"shape\":[1,29]}\n - fc1 (linear) params={\"inFeatures\":29,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":29,"outFeatures":843}},{"type":"update_params","name":"fc2","params":{"inFeatures":843,"outFeatures":843}},{"type":"update_params","name":"head","params":{"inFeatures":843,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":770502}} {"id":"gen-mlp-1460","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 274-feature input with 195 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1460\nComponents:\n - input (input) params={\"shape\":[1,274]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,274]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":274,"outFeatures":663}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":663,"outFeatures":475}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":475,"outFeatures":195}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":589212}} {"id":"gen-ae-1461","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1740-dim input: encode down to a 19-dim bottleneck and decode back to 1740. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1461\nComponents:\n - input (input) params={\"shape\":[1,1740]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1740]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1740,"outFeatures":173}},{"componentType":"linear","name":"enc2","params":{"inFeatures":173,"outFeatures":19}},{"componentType":"linear","name":"dec1","params":{"inFeatures":19,"outFeatures":173}},{"componentType":"linear","name":"dec2","params":{"inFeatures":173,"outFeatures":1740}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":608614}} {"id":"gen-cnn-1462","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 13 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1462\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":41,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":28,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":23820}} {"id":"gen-txf-1463","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 323-token sequences with 63 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1463\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,323]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18842,"embeddingDim":144}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":144,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":144,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2888208}} {"id":"gen-gqa-1464","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 445-token sequences with 80 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1464\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13229,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1277664}} {"id":"gen-fix-1465","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (140) is not divisible by numHeads (11). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1465\nComponents:\n - input (input) params={\"shape\":[1,487]}\n - embed (embedding) params={\"numEmbeddings\":31139,\"embeddingDim\":140}\n - attn (multiHeadAttention) params={\"embedDim\":140,\"numHeads\":11}\n - head (linear) params={\"inFeatures\":140,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":4444720}} {"id":"gen-trim-1466","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1590-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1466\nComponents:\n - input (input) params={\"shape\":[1,239]}\n - fc1 (linear) params={\"inFeatures\":239,\"outFeatures\":1590}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1590,\"outFeatures\":1590}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1590,\"outFeatures\":1590}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1590,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":239,"outFeatures":189}},{"type":"update_params","name":"fc2","params":{"inFeatures":189,"outFeatures":189}},{"type":"update_params","name":"fc3","params":{"inFeatures":189,"outFeatures":189}},{"type":"update_params","name":"head","params":{"inFeatures":189,"outFeatures":48}}],"grade":{"pass":true,"score":84,"params":125685}} {"id":"gen-norm-1467","family":"norm","seed":20260716,"spec":"This 233-feature, 99-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1467\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - fc1 (linear) params={\"inFeatures\":233,\"outFeatures\":231}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":231,\"outFeatures\":397}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":397,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":231}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":397}}],"grade":{"pass":true,"score":94,"params":184833}} {"id":"gen-tower-1468","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 203-feature user input and a 233-feature item input, each through its own 2-layer MLP tower ending at width 80, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1468\nComponents:\n - user_input (input) params={\"shape\":[1,203]}\n - item_input (input) params={\"shape\":[1,233]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,203]}},{"componentType":"input","name":"item_input","params":{"shape":[1,233]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":203,"outFeatures":80}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":80,"outFeatures":80}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":233,"outFeatures":80}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":80,"outFeatures":80}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":47840}} {"id":"gen-grow-1469","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (108-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1469\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":1169}},{"type":"update_params","name":"fc2","params":{"inFeatures":1169,"outFeatures":1169}},{"type":"update_params","name":"head","params":{"inFeatures":1169,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":1550094}} {"id":"gen-mlp-1470","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 393-feature input with 111 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1470\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":393,"outFeatures":703}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":703,"outFeatures":615}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":615,"outFeatures":111}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":776889}} {"id":"gen-ae-1471","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 867-dim input: encode down to a 29-dim bottleneck and decode back to 867. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1471\nComponents:\n - input (input) params={\"shape\":[1,867]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,867]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":867,"outFeatures":326}},{"componentType":"linear","name":"enc2","params":{"inFeatures":326,"outFeatures":29}},{"componentType":"linear","name":"dec1","params":{"inFeatures":29,"outFeatures":326}},{"componentType":"linear","name":"dec2","params":{"inFeatures":326,"outFeatures":867}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":584192}} {"id":"gen-cnn-1472","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 95 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1472\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":45,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":14,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":16,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":15115}} {"id":"gen-txf-1473","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 387-token sequences with 22 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1473\nComponents:\n - input (input) params={\"shape\":[1,387]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,387]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46906,"embeddingDim":60}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":60,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2830080}} {"id":"gen-gqa-1474","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 169-token sequences with 71 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1474\nComponents:\n - input (input) params={\"shape\":[1,169]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,169]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23930,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4416184}} {"id":"gen-fix-1475","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (126) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1475\nComponents:\n - input (input) params={\"shape\":[1,477]}\n - embed (embedding) params={\"numEmbeddings\":35484,\"embeddingDim\":126}\n - attn (multiHeadAttention) params={\"embedDim\":126,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":126,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4539528}} {"id":"gen-trim-1476","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1691-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1476\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":1691}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1691,\"outFeatures\":1691}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1691,\"outFeatures\":1691}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1691,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":67,"outFeatures":255}},{"type":"update_params","name":"fc2","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"fc3","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"head","params":{"inFeatures":255,"outFeatures":36}}],"grade":{"pass":true,"score":84,"params":156315}} {"id":"gen-norm-1477","family":"norm","seed":20260716,"spec":"This 226-feature, 34-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1477\nComponents:\n - input (input) params={\"shape\":[1,226]}\n - fc1 (linear) params={\"inFeatures\":226,\"outFeatures\":43}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":43,\"outFeatures\":403}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":403,\"outFeatures\":482}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":482,\"outFeatures\":496}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":496,\"outFeatures\":341}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":341,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":43}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":403}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":482}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":496}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":341}}],"grade":{"pass":true,"score":100,"params":641095}} {"id":"gen-tower-1478","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 236-feature user input and a 207-feature item input, each through its own 2-layer MLP tower ending at width 399, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1478\nComponents:\n - user_input (input) params={\"shape\":[1,236]}\n - item_input (input) params={\"shape\":[1,207]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,236]}},{"componentType":"input","name":"item_input","params":{"shape":[1,207]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":236,"outFeatures":399}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":399,"outFeatures":399}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":207,"outFeatures":399}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":399,"outFeatures":399}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":798,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":495957}} {"id":"gen-grow-1479","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (44-feature input, 48 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1479\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":766}},{"type":"update_params","name":"fc2","params":{"inFeatures":766,"outFeatures":766}},{"type":"update_params","name":"head","params":{"inFeatures":766,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":657228}} {"id":"gen-mlp-1480","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 446-feature input with 45 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1480\nComponents:\n - input (input) params={\"shape\":[1,446]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,446]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":446,"outFeatures":576}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":576,"outFeatures":831}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":831,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":772947}} {"id":"gen-ae-1481","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1709-dim input: encode down to a 252-dim bottleneck and decode back to 1709. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1481\nComponents:\n - input (input) params={\"shape\":[1,1709]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1709]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1709,"outFeatures":289}},{"componentType":"linear","name":"enc2","params":{"inFeatures":289,"outFeatures":252}},{"componentType":"linear","name":"dec1","params":{"inFeatures":252,"outFeatures":289}},{"componentType":"linear","name":"dec2","params":{"inFeatures":289,"outFeatures":1709}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1133458}} {"id":"gen-cnn-1482","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x41x41 image with 60 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1482\nComponents:\n - input (input) params={\"shape\":[1,3,41,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,41,41]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":12918}} {"id":"gen-txf-1483","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 193-token sequences with 5 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1483\nComponents:\n - input (input) params={\"shape\":[1,193]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,193]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36681,"embeddingDim":144}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":144,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":144,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":144,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5531616}} {"id":"gen-gqa-1484","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 201-token sequences with 55 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1484\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,201]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11205,"embeddingDim":752}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":752,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[752]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":752,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[752]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":752,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[752]}},{"componentType":"linear","name":"head","params":{"inFeatures":752,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":8467520}} {"id":"gen-fix-1485","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (324) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1485\nComponents:\n - input (input) params={\"shape\":[1,441]}\n - embed (embedding) params={\"numEmbeddings\":38444,\"embeddingDim\":324}\n - attn (multiHeadAttention) params={\"embedDim\":324,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":324,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":12895200}} {"id":"gen-trim-1486","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2757-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1486\nComponents:\n - input (input) params={\"shape\":[1,203]}\n - fc1 (linear) params={\"inFeatures\":203,\"outFeatures\":2757}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2757,\"outFeatures\":2757}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2757,\"outFeatures\":2757}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2757,\"outFeatures\":2757}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2757,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":203,"outFeatures":148}},{"type":"update_params","name":"fc2","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"fc3","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"fc4","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"head","params":{"inFeatures":148,"outFeatures":37}}],"grade":{"pass":true,"score":88,"params":101232}} {"id":"gen-norm-1487","family":"norm","seed":20260716,"spec":"This 35-feature, 57-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1487\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - fc1 (linear) params={\"inFeatures\":35,\"outFeatures\":188}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":188,\"outFeatures\":35}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":35,\"outFeatures\":33}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":33,\"outFeatures\":458}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":458,\"outFeatures\":57}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":188}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":35}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":33}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":458}}],"grade":{"pass":true,"score":100,"params":55535}} {"id":"gen-tower-1488","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 215-feature user input and a 35-feature item input, each through its own 2-layer MLP tower ending at width 448, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1488\nComponents:\n - user_input (input) params={\"shape\":[1,215]}\n - item_input (input) params={\"shape\":[1,35]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,215]}},{"componentType":"input","name":"item_input","params":{"shape":[1,35]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":215,"outFeatures":448}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":35,"outFeatures":448}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":896,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":514304}} {"id":"gen-grow-1489","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (72-feature input, 34 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1489\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":72,"outFeatures":1018}},{"type":"update_params","name":"fc2","params":{"inFeatures":1018,"outFeatures":1018}},{"type":"update_params","name":"head","params":{"inFeatures":1018,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":1144232}} {"id":"gen-mlp-1490","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 119-feature input with 131 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1490\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,119]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":119,"outFeatures":34}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":34,"outFeatures":351}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":351,"outFeatures":851}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":851,"outFeatures":633}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":633,"outFeatures":48}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":890036}} {"id":"gen-ae-1491","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1484-dim input: encode down to a 118-dim bottleneck and decode back to 1484. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1491\nComponents:\n - input (input) params={\"shape\":[1,1484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1484]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1484,"outFeatures":383}},{"componentType":"linear","name":"enc2","params":{"inFeatures":383,"outFeatures":118}},{"componentType":"linear","name":"dec1","params":{"inFeatures":118,"outFeatures":383}},{"componentType":"linear","name":"dec2","params":{"inFeatures":383,"outFeatures":1484}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1227132}} {"id":"gen-cnn-1492","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 87 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1492\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":34,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":16440}} {"id":"gen-txf-1493","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 367-token sequences with 7 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1493\nComponents:\n - input (input) params={\"shape\":[1,367]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,367]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34518,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3633864}} {"id":"gen-gqa-1494","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 97-token sequences with 97 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1494\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,97]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8265,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5217888}} {"id":"gen-fix-1495","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (338) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1495\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - embed (embedding) params={\"numEmbeddings\":12727,\"embeddingDim\":338}\n - attn (multiHeadAttention) params={\"embedDim\":338,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":338,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4763772}} {"id":"gen-trim-1496","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2980-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1496\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":2980}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2980,\"outFeatures\":2980}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2980,\"outFeatures\":2980}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2980,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":74}},{"type":"update_params","name":"fc2","params":{"inFeatures":74,"outFeatures":74}},{"type":"update_params","name":"fc3","params":{"inFeatures":74,"outFeatures":74}},{"type":"update_params","name":"head","params":{"inFeatures":74,"outFeatures":4}}],"grade":{"pass":true,"score":84,"params":14356}} {"id":"gen-norm-1497","family":"norm","seed":20260716,"spec":"This 91-feature, 99-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1497\nComponents:\n - input (input) params={\"shape\":[1,91]}\n - fc1 (linear) params={\"inFeatures\":91,\"outFeatures\":435}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":435,\"outFeatures\":276}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":276,\"outFeatures\":419}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":419,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":435}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":419}}],"grade":{"pass":true,"score":100,"params":316770}} {"id":"gen-tower-1498","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 127-feature user input and a 73-feature item input, each through its own 2-layer MLP tower ending at width 176, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1498\nComponents:\n - user_input (input) params={\"shape\":[1,127]}\n - item_input (input) params={\"shape\":[1,73]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,127]}},{"componentType":"input","name":"item_input","params":{"shape":[1,73]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":127,"outFeatures":176}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":176,"outFeatures":176}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":73,"outFeatures":176}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":176,"outFeatures":176}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":97504}} {"id":"gen-grow-1499","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1499\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":1255}},{"type":"update_params","name":"fc2","params":{"inFeatures":1255,"outFeatures":1255}},{"type":"update_params","name":"head","params":{"inFeatures":1255,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":1642795}} {"id":"gen-mlp-1500","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 484-feature input with 179 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1500\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":484,"outFeatures":127}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":127,"outFeatures":208}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":208,"outFeatures":987}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":987,"outFeatures":179}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":469853}} {"id":"gen-ae-1501","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 103-dim input: encode down to a 65-dim bottleneck and decode back to 103. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1501\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,103]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":103,"outFeatures":313}},{"componentType":"linear","name":"enc2","params":{"inFeatures":313,"outFeatures":65}},{"componentType":"linear","name":"dec1","params":{"inFeatures":65,"outFeatures":313}},{"componentType":"linear","name":"dec2","params":{"inFeatures":313,"outFeatures":103}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":105168}} {"id":"gen-cnn-1502","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 38 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1502\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":9,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":15183}} {"id":"gen-txf-1503","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 493-token sequences with 80 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1503\nComponents:\n - input (input) params={\"shape\":[1,493]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,493]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24273,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":176,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4533936}} {"id":"gen-gqa-1504","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 203-token sequences with 76 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1504\nComponents:\n - input (input) params={\"shape\":[1,203]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,203]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11275,"embeddingDim":264}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":264,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[264]}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2996664}} {"id":"gen-fix-1505","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (474) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1505\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - embed (embedding) params={\"numEmbeddings\":39083,\"embeddingDim\":474}\n - attn (multiHeadAttention) params={\"embedDim\":474,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":474,\"outFeatures\":52}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":19448694}} {"id":"gen-trim-1506","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1903-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1506\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":1903}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1903,\"outFeatures\":1903}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1903,\"outFeatures\":1903}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1903,\"outFeatures\":1903}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1903,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":255}},{"type":"update_params","name":"fc2","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"fc3","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"fc4","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"head","params":{"inFeatures":255,"outFeatures":2}}],"grade":{"pass":true,"score":88,"params":212160}} {"id":"gen-norm-1507","family":"norm","seed":20260716,"spec":"This 228-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1507\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - fc1 (linear) params={\"inFeatures\":228,\"outFeatures\":392}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":392,\"outFeatures\":217}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":217,\"outFeatures\":137}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":137,\"outFeatures\":409}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":409,\"outFeatures\":352}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":352,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":392}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":217}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":137}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":352}}],"grade":{"pass":true,"score":100,"params":420010}} {"id":"gen-tower-1508","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 95-feature user input and a 178-feature item input, each through its own 2-layer MLP tower ending at width 167, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1508\nComponents:\n - user_input (input) params={\"shape\":[1,95]}\n - item_input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,95]}},{"componentType":"input","name":"item_input","params":{"shape":[1,178]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":95,"outFeatures":167}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":167,"outFeatures":167}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":178,"outFeatures":167}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":167,"outFeatures":167}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":334,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":101703}} {"id":"gen-grow-1509","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (81-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1509\nComponents:\n - input (input) params={\"shape\":[1,81]}\n - fc1 (linear) params={\"inFeatures\":81,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":81,"outFeatures":1172}},{"type":"update_params","name":"fc2","params":{"inFeatures":1172,"outFeatures":1172}},{"type":"update_params","name":"head","params":{"inFeatures":1172,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":1474376}} {"id":"gen-mlp-1510","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 187-feature input with 151 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1510\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,187]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":187,"outFeatures":833}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":833,"outFeatures":782}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":782,"outFeatures":151}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":925259}} {"id":"gen-ae-1511","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 875-dim input: encode down to a 5-dim bottleneck and decode back to 875. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1511\nComponents:\n - input (input) params={\"shape\":[1,875]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,875]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":875,"outFeatures":708}},{"componentType":"linear","name":"enc2","params":{"inFeatures":708,"outFeatures":5}},{"componentType":"linear","name":"dec1","params":{"inFeatures":5,"outFeatures":708}},{"componentType":"linear","name":"dec2","params":{"inFeatures":708,"outFeatures":875}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1246080}} {"id":"gen-cnn-1512","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 22 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1512\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":12262}} {"id":"gen-txf-1513","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 277-token sequences with 35 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1513\nComponents:\n - input (input) params={\"shape\":[1,277]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,277]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49234,"embeddingDim":114}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":114,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":114,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":114,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5720634}} {"id":"gen-gqa-1514","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 255-token sequences with 63 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1514\nComponents:\n - input (input) params={\"shape\":[1,255]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,255]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42808,"embeddingDim":32}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":32,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[32]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":32,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[32]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":32,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[32]}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1371872}} {"id":"gen-fix-1515","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (186) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1515\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - embed (embedding) params={\"numEmbeddings\":9867,\"embeddingDim\":186}\n - attn (multiHeadAttention) params={\"embedDim\":186,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":186,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":1983690}} {"id":"gen-trim-1516","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3458-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1516\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":3458}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3458,\"outFeatures\":3458}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3458,\"outFeatures\":3458}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3458,\"outFeatures\":3458}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3458,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":70,"outFeatures":245}},{"type":"update_params","name":"fc2","params":{"inFeatures":245,"outFeatures":245}},{"type":"update_params","name":"fc3","params":{"inFeatures":245,"outFeatures":245}},{"type":"update_params","name":"fc4","params":{"inFeatures":245,"outFeatures":245}},{"type":"update_params","name":"head","params":{"inFeatures":245,"outFeatures":9}}],"grade":{"pass":true,"score":88,"params":199430}} {"id":"gen-norm-1517","family":"norm","seed":20260716,"spec":"This 206-feature, 40-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1517\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":480}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":480,\"outFeatures\":94}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":94,\"outFeatures\":438}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":438,\"outFeatures\":284}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":284,\"outFeatures\":333}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":333,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":480}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":94}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":438}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":284}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":333}}],"grade":{"pass":true,"score":100,"params":417456}} {"id":"gen-tower-1518","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 58-feature user input and a 226-feature item input, each through its own 2-layer MLP tower ending at width 291, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1518\nComponents:\n - user_input (input) params={\"shape\":[1,58]}\n - item_input (input) params={\"shape\":[1,226]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,58]}},{"componentType":"input","name":"item_input","params":{"shape":[1,226]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":58,"outFeatures":291}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":226,"outFeatures":291}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":291,"outFeatures":291}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":582,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":252588}} {"id":"gen-grow-1519","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (71-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1519\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - fc1 (linear) params={\"inFeatures\":71,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":71,"outFeatures":1386}},{"type":"update_params","name":"fc2","params":{"inFeatures":1386,"outFeatures":1386}},{"type":"update_params","name":"head","params":{"inFeatures":1386,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":2045736}} {"id":"gen-mlp-1520","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 406-feature input with 152 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1520\nComponents:\n - input (input) params={\"shape\":[1,406]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,406]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":406,"outFeatures":274}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":274,"outFeatures":947}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":947,"outFeatures":121}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":121,"outFeatures":152}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":503701}} {"id":"gen-ae-1521","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 885-dim input: encode down to a 145-dim bottleneck and decode back to 885. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1521\nComponents:\n - input (input) params={\"shape\":[1,885]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,885]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":885,"outFeatures":690}},{"componentType":"linear","name":"enc2","params":{"inFeatures":690,"outFeatures":145}},{"componentType":"linear","name":"dec1","params":{"inFeatures":145,"outFeatures":690}},{"componentType":"linear","name":"dec2","params":{"inFeatures":690,"outFeatures":885}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1421400}} {"id":"gen-cnn-1522","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 70 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1522\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7995}} {"id":"gen-txf-1523","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 398-token sequences with 93 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1523\nComponents:\n - input (input) params={\"shape\":[1,398]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,398]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19691,"embeddingDim":148}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":148,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":148,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3015648}} {"id":"gen-gqa-1524","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 304-token sequences with 80 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1524\nComponents:\n - input (input) params={\"shape\":[1,304]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,304]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33462,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":272,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":9123424}} {"id":"gen-trim-1526","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2848-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1526\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - fc1 (linear) params={\"inFeatures\":251,\"outFeatures\":2848}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2848,\"outFeatures\":2848}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2848,\"outFeatures\":2848}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2848,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":251,"outFeatures":353}},{"type":"update_params","name":"fc2","params":{"inFeatures":353,"outFeatures":353}},{"type":"update_params","name":"fc3","params":{"inFeatures":353,"outFeatures":353}},{"type":"update_params","name":"head","params":{"inFeatures":353,"outFeatures":20}}],"grade":{"pass":true,"score":84,"params":344881}} {"id":"gen-norm-1527","family":"norm","seed":20260716,"spec":"This 150-feature, 25-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1527\nComponents:\n - input (input) params={\"shape\":[1,150]}\n - fc1 (linear) params={\"inFeatures\":150,\"outFeatures\":245}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":245,\"outFeatures\":259}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":259,\"outFeatures\":434}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":434,\"outFeatures\":316}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":316,\"outFeatures\":322}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":322,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":245}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":259}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":434}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":316}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":322}}],"grade":{"pass":true,"score":100,"params":459557}} {"id":"gen-tower-1528","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 89-feature user input and a 189-feature item input, each through its own 2-layer MLP tower ending at width 374, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1528\nComponents:\n - user_input (input) params={\"shape\":[1,89]}\n - item_input (input) params={\"shape\":[1,189]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,89]}},{"componentType":"input","name":"item_input","params":{"shape":[1,189]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":89,"outFeatures":374}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":374,"outFeatures":374}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":189,"outFeatures":374}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":374,"outFeatures":374}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":748,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":384472}} {"id":"gen-grow-1529","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 17 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1529\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":760}},{"type":"update_params","name":"fc2","params":{"inFeatures":760,"outFeatures":760}},{"type":"update_params","name":"head","params":{"inFeatures":760,"outFeatures":17}}],"grade":{"pass":true,"score":80,"params":637640}} {"id":"gen-mlp-1530","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 282-feature input with 10 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1530\nComponents:\n - input (input) params={\"shape\":[1,282]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,282]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":282,"outFeatures":506}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":506,"outFeatures":564}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":564,"outFeatures":536}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":536,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":735740}} {"id":"gen-ae-1531","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1969-dim input: encode down to a 140-dim bottleneck and decode back to 1969. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1531\nComponents:\n - input (input) params={\"shape\":[1,1969]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1969]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1969,"outFeatures":889}},{"componentType":"linear","name":"enc2","params":{"inFeatures":889,"outFeatures":140}},{"componentType":"linear","name":"dec1","params":{"inFeatures":140,"outFeatures":889}},{"componentType":"linear","name":"dec2","params":{"inFeatures":889,"outFeatures":1969}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3749802}} {"id":"gen-cnn-1532","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x30x30 image with 79 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1532\nComponents:\n - input (input) params={\"shape\":[1,3,30,30]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,30,30]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":13,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":48,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":53,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":33851}} {"id":"gen-txf-1533","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 397-token sequences with 9 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1533\nComponents:\n - input (input) params={\"shape\":[1,397]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,397]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15341,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1245972}} {"id":"gen-gqa-1534","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 352-token sequences with 55 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1534\nComponents:\n - input (input) params={\"shape\":[1,352]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,352]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39657,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":14614016}} {"id":"gen-trim-1536","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3793-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1536\nComponents:\n - input (input) params={\"shape\":[1,204]}\n - fc1 (linear) params={\"inFeatures\":204,\"outFeatures\":3793}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3793,\"outFeatures\":3793}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3793,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":204,"outFeatures":76}},{"type":"update_params","name":"fc2","params":{"inFeatures":76,"outFeatures":76}},{"type":"update_params","name":"head","params":{"inFeatures":76,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":24168}} {"id":"gen-norm-1537","family":"norm","seed":20260716,"spec":"This 72-feature, 62-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1537\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":426}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":426,\"outFeatures\":377}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":377,\"outFeatures\":479}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":479,\"outFeatures\":62}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":426}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":377}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":479}}],"grade":{"pass":true,"score":100,"params":401555}} {"id":"gen-tower-1538","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 173-feature user input and a 215-feature item input, each through its own 2-layer MLP tower ending at width 376, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1538\nComponents:\n - user_input (input) params={\"shape\":[1,173]}\n - item_input (input) params={\"shape\":[1,215]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,173]}},{"componentType":"input","name":"item_input","params":{"shape":[1,215]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":173,"outFeatures":376}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":376,"outFeatures":376}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":215,"outFeatures":376}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":376,"outFeatures":376}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":752,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":429392}} {"id":"gen-grow-1539","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (97-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1539\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":97,"outFeatures":1025}},{"type":"update_params","name":"fc2","params":{"inFeatures":1025,"outFeatures":1025}},{"type":"update_params","name":"head","params":{"inFeatures":1025,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":1166450}} {"id":"gen-mlp-1540","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 455-feature input with 75 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1540\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,455]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":455,"outFeatures":147}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":147,"outFeatures":243}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":243,"outFeatures":670}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":670,"outFeatures":665}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":665,"outFeatures":919}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":919,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1391026}} {"id":"gen-ae-1541","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1419-dim input: encode down to a 87-dim bottleneck and decode back to 1419. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1541\nComponents:\n - input (input) params={\"shape\":[1,1419]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1419]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1419,"outFeatures":236}},{"componentType":"linear","name":"enc2","params":{"inFeatures":236,"outFeatures":87}},{"componentType":"linear","name":"dec1","params":{"inFeatures":87,"outFeatures":236}},{"componentType":"linear","name":"dec2","params":{"inFeatures":236,"outFeatures":1419}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":710832}} {"id":"gen-cnn-1542","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 34 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1542\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":44,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":16,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":15374}} {"id":"gen-txf-1543","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 170-token sequences with 24 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1543\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,170]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9380,"embeddingDim":100}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":100,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":100,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1020400}} {"id":"gen-gqa-1544","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 346-token sequences with 31 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1544\nComponents:\n - input (input) params={\"shape\":[1,346]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,346]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18897,"embeddingDim":232}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":232,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[232]}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4391296}} {"id":"gen-trim-1546","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3183-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1546\nComponents:\n - input (input) params={\"shape\":[1,233]}\n - fc1 (linear) params={\"inFeatures\":233,\"outFeatures\":3183}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3183,\"outFeatures\":3183}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3183,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":233,"outFeatures":261}},{"type":"update_params","name":"fc2","params":{"inFeatures":261,"outFeatures":261}},{"type":"update_params","name":"head","params":{"inFeatures":261,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":137286}} {"id":"gen-norm-1547","family":"norm","seed":20260716,"spec":"This 102-feature, 75-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1547\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - fc1 (linear) params={\"inFeatures\":102,\"outFeatures\":243}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":243,\"outFeatures\":355}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":355,\"outFeatures\":75}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":243}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":355}}],"grade":{"pass":true,"score":94,"params":137676}} {"id":"gen-tower-1548","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 159-feature user input and a 105-feature item input, each through its own 2-layer MLP tower ending at width 434, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1548\nComponents:\n - user_input (input) params={\"shape\":[1,159]}\n - item_input (input) params={\"shape\":[1,105]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,159]}},{"componentType":"input","name":"item_input","params":{"shape":[1,105]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":159,"outFeatures":434}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":434,"outFeatures":434}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":105,"outFeatures":434}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":434,"outFeatures":434}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":868,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":492156}} {"id":"gen-grow-1549","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (32-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1549\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":1176}},{"type":"update_params","name":"fc2","params":{"inFeatures":1176,"outFeatures":1176}},{"type":"update_params","name":"head","params":{"inFeatures":1176,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":1465296}} {"id":"gen-mlp-1550","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 161-feature input with 134 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1550\nComponents:\n - input (input) params={\"shape\":[1,161]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,161]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":161,"outFeatures":66}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":66,"outFeatures":84}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":84,"outFeatures":734}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":734,"outFeatures":287}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":287,"outFeatures":759}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":759,"outFeatures":134}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":608023}} {"id":"gen-ae-1551","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 123-dim input: encode down to a 236-dim bottleneck and decode back to 123. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1551\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,123]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":123,"outFeatures":643}},{"componentType":"linear","name":"enc2","params":{"inFeatures":643,"outFeatures":236}},{"componentType":"linear","name":"dec1","params":{"inFeatures":236,"outFeatures":643}},{"componentType":"linear","name":"dec2","params":{"inFeatures":643,"outFeatures":123}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":461674}} {"id":"gen-cnn-1552","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 40 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1552\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1224}} {"id":"gen-txf-1553","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 504-token sequences with 91 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1553\nComponents:\n - input (input) params={\"shape\":[1,504]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,504]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15127,"embeddingDim":232}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":232,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":232,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3961168}} {"id":"gen-gqa-1554","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 307-token sequences with 3 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1554\nComponents:\n - input (input) params={\"shape\":[1,307]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,307]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9811,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1727264}} {"id":"gen-trim-1556","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1884-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1556\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":1884}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1884,\"outFeatures\":1884}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1884,\"outFeatures\":1884}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1884,\"outFeatures\":1884}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1884,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":378}},{"type":"update_params","name":"fc2","params":{"inFeatures":378,"outFeatures":378}},{"type":"update_params","name":"fc3","params":{"inFeatures":378,"outFeatures":378}},{"type":"update_params","name":"fc4","params":{"inFeatures":378,"outFeatures":378}},{"type":"update_params","name":"head","params":{"inFeatures":378,"outFeatures":49}}],"grade":{"pass":true,"score":88,"params":459648}} {"id":"gen-norm-1557","family":"norm","seed":20260716,"spec":"This 165-feature, 31-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1557\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - fc1 (linear) params={\"inFeatures\":165,\"outFeatures\":202}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":202,\"outFeatures\":369}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":369,\"outFeatures\":229}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":229,\"outFeatures\":245}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":245,\"outFeatures\":337}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":337,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":202}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":369}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":229}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":245}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":337}}],"grade":{"pass":true,"score":100,"params":341486}} {"id":"gen-tower-1558","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 152-feature user input and a 253-feature item input, each through its own 2-layer MLP tower ending at width 63, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1558\nComponents:\n - user_input (input) params={\"shape\":[1,152]}\n - item_input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,152]}},{"componentType":"input","name":"item_input","params":{"shape":[1,253]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":152,"outFeatures":63}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":63,"outFeatures":63}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":253,"outFeatures":63}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":63,"outFeatures":63}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":126,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":33579}} {"id":"gen-grow-1559","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (122-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1559\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":122,"outFeatures":1382}},{"type":"update_params","name":"fc2","params":{"inFeatures":1382,"outFeatures":1382}},{"type":"update_params","name":"head","params":{"inFeatures":1382,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":2104786}} {"id":"gen-mlp-1560","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 271-feature input with 93 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1560\nComponents:\n - input (input) params={\"shape\":[1,271]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,271]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":271,"outFeatures":173}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":173,"outFeatures":893}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":893,"outFeatures":405}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":405,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":600702}} {"id":"gen-ae-1561","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 455-dim input: encode down to a 59-dim bottleneck and decode back to 455. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1561\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,455]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":455,"outFeatures":620}},{"componentType":"linear","name":"enc2","params":{"inFeatures":620,"outFeatures":59}},{"componentType":"linear","name":"dec1","params":{"inFeatures":59,"outFeatures":620}},{"componentType":"linear","name":"dec2","params":{"inFeatures":620,"outFeatures":455}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":637360}} {"id":"gen-cnn-1562","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 50 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1562\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":45,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":18,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12745}} {"id":"gen-txf-1563","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 227-token sequences with 13 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1563\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18515,"embeddingDim":22}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":22,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":22,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":22,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":413424}} {"id":"gen-gqa-1564","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 431-token sequences with 90 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1564\nComponents:\n - input (input) params={\"shape\":[1,431]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,431]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35589,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6279504}} {"id":"gen-fix-1565","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (308) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1565\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - embed (embedding) params={\"numEmbeddings\":48782,\"embeddingDim\":308}\n - attn (multiHeadAttention) params={\"embedDim\":308,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":308,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":15406468}} {"id":"gen-trim-1566","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2382-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1566\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":2382}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2382,\"outFeatures\":2382}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2382,\"outFeatures\":2382}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2382,\"outFeatures\":2382}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2382,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":220,"outFeatures":155}},{"type":"update_params","name":"fc2","params":{"inFeatures":155,"outFeatures":155}},{"type":"update_params","name":"fc3","params":{"inFeatures":155,"outFeatures":155}},{"type":"update_params","name":"fc4","params":{"inFeatures":155,"outFeatures":155}},{"type":"update_params","name":"head","params":{"inFeatures":155,"outFeatures":39}}],"grade":{"pass":true,"score":88,"params":112220}} {"id":"gen-norm-1567","family":"norm","seed":20260716,"spec":"This 110-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1567\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":223}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":223,\"outFeatures\":441}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":441,\"outFeatures\":134}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":134,\"outFeatures\":321}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":321,\"outFeatures\":380}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":380,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":223}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":441}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":134}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":321}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":380}}],"grade":{"pass":true,"score":100,"params":376601}} {"id":"gen-tower-1568","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 51-feature user input and a 189-feature item input, each through its own 2-layer MLP tower ending at width 508, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1568\nComponents:\n - user_input (input) params={\"shape\":[1,51]}\n - item_input (input) params={\"shape\":[1,189]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,51]}},{"componentType":"input","name":"item_input","params":{"shape":[1,189]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":51,"outFeatures":508}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":508,"outFeatures":508}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":189,"outFeatures":508}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":508,"outFeatures":508}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1016,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":639064}} {"id":"gen-grow-1569","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1569\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":1154}},{"type":"update_params","name":"fc2","params":{"inFeatures":1154,"outFeatures":1154}},{"type":"update_params","name":"head","params":{"inFeatures":1154,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":1475966}} {"id":"gen-mlp-1570","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 493-feature input with 173 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1570\nComponents:\n - input (input) params={\"shape\":[1,493]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,493]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":493,"outFeatures":724}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":724,"outFeatures":875}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":875,"outFeatures":525}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":525,"outFeatures":967}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":967,"outFeatures":392}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":392,"outFeatures":1008}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1008,"outFeatures":173}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2906066}} {"id":"gen-ae-1571","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 961-dim input: encode down to a 203-dim bottleneck and decode back to 961. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1571\nComponents:\n - input (input) params={\"shape\":[1,961]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,961]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":961,"outFeatures":539}},{"componentType":"linear","name":"enc2","params":{"inFeatures":539,"outFeatures":203}},{"componentType":"linear","name":"dec1","params":{"inFeatures":203,"outFeatures":539}},{"componentType":"linear","name":"dec2","params":{"inFeatures":539,"outFeatures":961}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1254792}} {"id":"gen-cnn-1572","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 31 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1572\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":27,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30158}} {"id":"gen-txf-1573","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 275-token sequences with 91 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1573\nComponents:\n - input (input) params={\"shape\":[1,275]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,275]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32546,"embeddingDim":140}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":140,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":140,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4647580}} {"id":"gen-gqa-1574","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 163-token sequences with 84 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1574\nComponents:\n - input (input) params={\"shape\":[1,163]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,163]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20492,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":84}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3292160}} {"id":"gen-fix-1575","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (490) is not divisible by numHeads (11). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1575\nComponents:\n - input (input) params={\"shape\":[1,167]}\n - embed (embedding) params={\"numEmbeddings\":43617,\"embeddingDim\":490}\n - attn (multiHeadAttention) params={\"embedDim\":490,\"numHeads\":11}\n - head (linear) params={\"inFeatures\":490,\"outFeatures\":56}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":22360170}} {"id":"gen-trim-1576","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2770-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1576\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":2770}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2770,\"outFeatures\":2770}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2770,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":381}},{"type":"update_params","name":"fc2","params":{"inFeatures":381,"outFeatures":381}},{"type":"update_params","name":"head","params":{"inFeatures":381,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":201549}} {"id":"gen-norm-1577","family":"norm","seed":20260716,"spec":"This 98-feature, 97-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1577\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":433}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":433,\"outFeatures\":61}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":61,\"outFeatures\":457}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":457,\"outFeatures\":97}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":433}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":61}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":457}}],"grade":{"pass":true,"score":100,"params":141053}} {"id":"gen-tower-1578","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 30-feature user input and a 118-feature item input, each through its own 2-layer MLP tower ending at width 200, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1578\nComponents:\n - user_input (input) params={\"shape\":[1,30]}\n - item_input (input) params={\"shape\":[1,118]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,30]}},{"componentType":"input","name":"item_input","params":{"shape":[1,118]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":30,"outFeatures":200}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":200,"outFeatures":200}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":118,"outFeatures":200}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":200,"outFeatures":200}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":110000}} {"id":"gen-grow-1579","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (97-feature input, 30 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1579\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":97,"outFeatures":693}},{"type":"update_params","name":"fc2","params":{"inFeatures":693,"outFeatures":693}},{"type":"update_params","name":"head","params":{"inFeatures":693,"outFeatures":30}}],"grade":{"pass":true,"score":80,"params":568260}} {"id":"gen-mlp-1580","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 271-feature input with 198 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1580\nComponents:\n - input (input) params={\"shape\":[1,271]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,271]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":271,"outFeatures":441}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":441,"outFeatures":297}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":297,"outFeatures":278}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":278,"outFeatures":871}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":871,"outFeatures":436}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":436,"outFeatures":198}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1041276}} {"id":"gen-ae-1581","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1823-dim input: encode down to a 187-dim bottleneck and decode back to 1823. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1581\nComponents:\n - input (input) params={\"shape\":[1,1823]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1823]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1823,"outFeatures":374}},{"componentType":"linear","name":"enc2","params":{"inFeatures":374,"outFeatures":187}},{"componentType":"linear","name":"dec1","params":{"inFeatures":187,"outFeatures":374}},{"componentType":"linear","name":"dec2","params":{"inFeatures":374,"outFeatures":1823}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1503480}} {"id":"gen-cnn-1582","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x30x30 image with 61 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1582\nComponents:\n - input (input) params={\"shape\":[1,3,30,30]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,30,30]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":61}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":21453}} {"id":"gen-txf-1583","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 418-token sequences with 4 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1583\nComponents:\n - input (input) params={\"shape\":[1,418]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,418]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8195,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":116,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1058732}} {"id":"gen-gqa-1584","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 68-token sequences with 40 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1584\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,68]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18110,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":11325600}} {"id":"gen-trim-1586","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4082-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1586\nComponents:\n - input (input) params={\"shape\":[1,249]}\n - fc1 (linear) params={\"inFeatures\":249,\"outFeatures\":4082}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4082,\"outFeatures\":4082}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4082,\"outFeatures\":4082}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4082,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":249,"outFeatures":242}},{"type":"update_params","name":"fc2","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"fc3","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"head","params":{"inFeatures":242,"outFeatures":44}}],"grade":{"pass":true,"score":84,"params":188034}} {"id":"gen-norm-1587","family":"norm","seed":20260716,"spec":"This 98-feature, 3-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1587\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":159}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":159,\"outFeatures\":196}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":196,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":159}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":196}}],"grade":{"pass":true,"score":94,"params":47334}} {"id":"gen-tower-1588","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 68-feature user input and a 92-feature item input, each through its own 2-layer MLP tower ending at width 184, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1588\nComponents:\n - user_input (input) params={\"shape\":[1,68]}\n - item_input (input) params={\"shape\":[1,92]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,68]}},{"componentType":"input","name":"item_input","params":{"shape":[1,92]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":68,"outFeatures":184}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":184,"outFeatures":184}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":92,"outFeatures":184}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":184,"outFeatures":184}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":97520}} {"id":"gen-grow-1589","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (96-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1589\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - fc1 (linear) params={\"inFeatures\":96,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":96,"outFeatures":1147}},{"type":"update_params","name":"fc2","params":{"inFeatures":1147,"outFeatures":1147}},{"type":"update_params","name":"head","params":{"inFeatures":1147,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":1429162}} {"id":"gen-mlp-1590","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 434-feature input with 52 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1590\nComponents:\n - input (input) params={\"shape\":[1,434]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,434]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":434,"outFeatures":1012}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":1012,"outFeatures":451}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":451,"outFeatures":51}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":921273}} {"id":"gen-ae-1591","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 450-dim input: encode down to a 111-dim bottleneck and decode back to 450. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1591\nComponents:\n - input (input) params={\"shape\":[1,450]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,450]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":450,"outFeatures":119}},{"componentType":"linear","name":"enc2","params":{"inFeatures":119,"outFeatures":111}},{"componentType":"linear","name":"dec1","params":{"inFeatures":111,"outFeatures":119}},{"componentType":"linear","name":"dec2","params":{"inFeatures":119,"outFeatures":450}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":133518}} {"id":"gen-cnn-1592","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 95 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1592\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":14849}} {"id":"gen-txf-1593","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 430-token sequences with 63 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1593\nComponents:\n - input (input) params={\"shape\":[1,430]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,430]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30328,"embeddingDim":488}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":488,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":488,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":17688536}} {"id":"gen-gqa-1594","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 424-token sequences with 22 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1594\nComponents:\n - input (input) params={\"shape\":[1,424]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,424]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29018,"embeddingDim":240}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":240,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":240,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[240]}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6969600}} {"id":"gen-fix-1595","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (174) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1595\nComponents:\n - input (input) params={\"shape\":[1,371]}\n - embed (embedding) params={\"numEmbeddings\":12318,\"embeddingDim\":174}\n - attn (multiHeadAttention) params={\"embedDim\":174,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":174,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2276094}} {"id":"gen-trim-1596","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 4078-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1596\nComponents:\n - input (input) params={\"shape\":[1,146]}\n - fc1 (linear) params={\"inFeatures\":146,\"outFeatures\":4078}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4078,\"outFeatures\":4078}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4078,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":146,"outFeatures":226}},{"type":"update_params","name":"fc2","params":{"inFeatures":226,"outFeatures":226}},{"type":"update_params","name":"head","params":{"inFeatures":226,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":89270}} {"id":"gen-norm-1597","family":"norm","seed":20260716,"spec":"This 105-feature, 82-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1597\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - fc1 (linear) params={\"inFeatures\":105,\"outFeatures\":200}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":200,\"outFeatures\":432}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":432,\"outFeatures\":401}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":401,\"outFeatures\":333}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":333,\"outFeatures\":82}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":200}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":432}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":401}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":333}}],"grade":{"pass":true,"score":100,"params":441471}} {"id":"gen-tower-1598","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 36-feature user input and a 29-feature item input, each through its own 2-layer MLP tower ending at width 423, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1598\nComponents:\n - user_input (input) params={\"shape\":[1,36]}\n - item_input (input) params={\"shape\":[1,29]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,36]}},{"componentType":"input","name":"item_input","params":{"shape":[1,29]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":36,"outFeatures":423}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":423,"outFeatures":423}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":29,"outFeatures":423}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":423,"outFeatures":423}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":846,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":386199}} {"id":"gen-grow-1599","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (58-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1599\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - fc1 (linear) params={\"inFeatures\":58,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":58,"outFeatures":1342}},{"type":"update_params","name":"fc2","params":{"inFeatures":1342,"outFeatures":1342}},{"type":"update_params","name":"head","params":{"inFeatures":1342,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1936506}} {"id":"gen-mlp-1600","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 374-feature input with 52 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1600\nComponents:\n - input (input) params={\"shape\":[1,374]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,374]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":374,"outFeatures":637}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":637,"outFeatures":886}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":886,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":848692}} {"id":"gen-ae-1601","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1045-dim input: encode down to a 137-dim bottleneck and decode back to 1045. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1601\nComponents:\n - input (input) params={\"shape\":[1,1045]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1045]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1045,"outFeatures":1002}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1002,"outFeatures":137}},{"componentType":"linear","name":"dec1","params":{"inFeatures":137,"outFeatures":1002}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1002,"outFeatures":1045}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2368728}} {"id":"gen-cnn-1602","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 52 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1602\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":8,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":57,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":27996}} {"id":"gen-txf-1603","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 490-token sequences with 17 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1603\nComponents:\n - input (input) params={\"shape\":[1,490]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,490]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16967,"embeddingDim":20}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":20,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":20,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":342880}} {"id":"gen-gqa-1604","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 492-token sequences with 63 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1604\nComponents:\n - input (input) params={\"shape\":[1,492]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,492]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49821,"embeddingDim":416}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":416,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[416]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":416,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[416]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":416,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[416]}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":20751744}} {"id":"gen-trim-1606","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3335-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1606\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":3335}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3335,\"outFeatures\":3335}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3335,\"outFeatures\":3335}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3335,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":112}},{"type":"update_params","name":"fc2","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"fc3","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"head","params":{"inFeatures":112,"outFeatures":44}}],"grade":{"pass":true,"score":84,"params":35616}} {"id":"gen-norm-1607","family":"norm","seed":20260716,"spec":"This 253-feature, 58-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1607\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - fc1 (linear) params={\"inFeatures\":253,\"outFeatures\":197}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":197,\"outFeatures\":453}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":453,\"outFeatures\":266}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":266,\"outFeatures\":58}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":197}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":453}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":266}}],"grade":{"pass":true,"score":100,"params":275008}} {"id":"gen-tower-1608","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 208-feature user input and a 174-feature item input, each through its own 2-layer MLP tower ending at width 175, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1608\nComponents:\n - user_input (input) params={\"shape\":[1,208]}\n - item_input (input) params={\"shape\":[1,174]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,208]}},{"componentType":"input","name":"item_input","params":{"shape":[1,174]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":208,"outFeatures":175}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":175,"outFeatures":175}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":174,"outFeatures":175}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":175,"outFeatures":175}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":350,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":128450}} {"id":"gen-mlp-1610","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 115-feature input with 22 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1610\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,115]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":115,"outFeatures":613}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":613,"outFeatures":960}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":960,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":680095}} {"id":"gen-ae-1611","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 979-dim input: encode down to a 54-dim bottleneck and decode back to 979. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1611\nComponents:\n - input (input) params={\"shape\":[1,979]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,979]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":979,"outFeatures":360}},{"componentType":"linear","name":"enc2","params":{"inFeatures":360,"outFeatures":54}},{"componentType":"linear","name":"dec1","params":{"inFeatures":54,"outFeatures":360}},{"componentType":"linear","name":"dec2","params":{"inFeatures":360,"outFeatures":979}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":743760}} {"id":"gen-cnn-1612","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 99 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1612\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":44,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":17,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":23544}} {"id":"gen-txf-1613","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 281-token sequences with 55 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1613\nComponents:\n - input (input) params={\"shape\":[1,281]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,281]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13294,"embeddingDim":44}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":44,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":595100}} {"id":"gen-gqa-1614","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 106-token sequences with 23 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1614\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,106]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26672,"embeddingDim":72}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":72,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[72]}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1922040}} {"id":"gen-trim-1616","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2620-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1616\nComponents:\n - input (input) params={\"shape\":[1,85]}\n - fc1 (linear) params={\"inFeatures\":85,\"outFeatures\":2620}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2620,\"outFeatures\":2620}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2620,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":85,"outFeatures":252}},{"type":"update_params","name":"fc2","params":{"inFeatures":252,"outFeatures":252}},{"type":"update_params","name":"head","params":{"inFeatures":252,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":92736}} {"id":"gen-norm-1617","family":"norm","seed":20260716,"spec":"This 70-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1617\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":330}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":330,\"outFeatures\":276}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":276,\"outFeatures\":330}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":330,\"outFeatures\":102}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":102,\"outFeatures\":351}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":351,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":330}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":276}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":330}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":102}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":351}}],"grade":{"pass":true,"score":100,"params":305259}} {"id":"gen-tower-1618","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 103-feature user input and a 248-feature item input, each through its own 2-layer MLP tower ending at width 319, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1618\nComponents:\n - user_input (input) params={\"shape\":[1,103]}\n - item_input (input) params={\"shape\":[1,248]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,103]}},{"componentType":"input","name":"item_input","params":{"shape":[1,248]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":103,"outFeatures":319}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":319,"outFeatures":319}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":248,"outFeatures":319}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":319,"outFeatures":319}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":638,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":316129}} {"id":"gen-grow-1619","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (88-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1619\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":88,"outFeatures":870}},{"type":"update_params","name":"fc2","params":{"inFeatures":870,"outFeatures":870}},{"type":"update_params","name":"head","params":{"inFeatures":870,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":849990}} {"id":"gen-mlp-1620","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 36-feature input with 199 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1620\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,36]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":36,"outFeatures":530}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":530,"outFeatures":446}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":446,"outFeatures":777}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":777,"outFeatures":842}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":842,"outFeatures":199}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1423794}} {"id":"gen-ae-1621","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 594-dim input: encode down to a 5-dim bottleneck and decode back to 594. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1621\nComponents:\n - input (input) params={\"shape\":[1,594]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,594]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":594,"outFeatures":675}},{"componentType":"linear","name":"enc2","params":{"inFeatures":675,"outFeatures":5}},{"componentType":"linear","name":"dec1","params":{"inFeatures":5,"outFeatures":675}},{"componentType":"linear","name":"dec2","params":{"inFeatures":675,"outFeatures":594}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":808650}} {"id":"gen-cnn-1622","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 37 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1622\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":52,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46031}} {"id":"gen-txf-1623","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 429-token sequences with 64 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1623\nComponents:\n - input (input) params={\"shape\":[1,429]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,429]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37913,"embeddingDim":32}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":32,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1219360}} {"id":"gen-gqa-1624","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 179-token sequences with 58 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1624\nComponents:\n - input (input) params={\"shape\":[1,179]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,179]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16692,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1340000}} {"id":"gen-trim-1626","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3376-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1626\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - fc1 (linear) params={\"inFeatures\":89,\"outFeatures\":3376}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3376,\"outFeatures\":3376}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3376,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":89,"outFeatures":210}},{"type":"update_params","name":"fc2","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"head","params":{"inFeatures":210,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":71190}} {"id":"gen-norm-1627","family":"norm","seed":20260716,"spec":"This 211-feature, 94-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1627\nComponents:\n - input (input) params={\"shape\":[1,211]}\n - fc1 (linear) params={\"inFeatures\":211,\"outFeatures\":441}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":441,\"outFeatures\":441}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":441,\"outFeatures\":94}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":441}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":441}}],"grade":{"pass":true,"score":94,"params":328986}} {"id":"gen-tower-1628","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 247-feature user input and a 212-feature item input, each through its own 2-layer MLP tower ending at width 208, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1628\nComponents:\n - user_input (input) params={\"shape\":[1,247]}\n - item_input (input) params={\"shape\":[1,212]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,247]}},{"componentType":"input","name":"item_input","params":{"shape":[1,212]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":247,"outFeatures":208}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":208,"outFeatures":208}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":212,"outFeatures":208}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":208,"outFeatures":208}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":182416}} {"id":"gen-grow-1629","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (55-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1629\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - fc1 (linear) params={\"inFeatures\":55,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":55,"outFeatures":1301}},{"type":"update_params","name":"fc2","params":{"inFeatures":1301,"outFeatures":1301}},{"type":"update_params","name":"head","params":{"inFeatures":1301,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":1816196}} {"id":"gen-mlp-1630","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 21-feature input with 143 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1630\nComponents:\n - input (input) params={\"shape\":[1,21]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,21]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":21,"outFeatures":774}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":774,"outFeatures":355}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":355,"outFeatures":518}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":518,"outFeatures":444}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":444,"outFeatures":143}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":768398}} {"id":"gen-ae-1631","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 724-dim input: encode down to a 61-dim bottleneck and decode back to 724. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1631\nComponents:\n - input (input) params={\"shape\":[1,724]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,724]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":724,"outFeatures":187}},{"componentType":"linear","name":"enc2","params":{"inFeatures":187,"outFeatures":61}},{"componentType":"linear","name":"dec1","params":{"inFeatures":61,"outFeatures":187}},{"componentType":"linear","name":"dec2","params":{"inFeatures":187,"outFeatures":724}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":293590}} {"id":"gen-cnn-1632","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 45 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1632\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":41,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":22,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":64,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":45}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":40527}} {"id":"gen-txf-1633","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 214-token sequences with 42 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1633\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,214]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29725,"embeddingDim":312}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":312,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":312,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":312,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":312,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":10455432}} {"id":"gen-gqa-1634","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 453-token sequences with 65 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1634\nComponents:\n - input (input) params={\"shape\":[1,453]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,453]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18743,"embeddingDim":240}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":240,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":240,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[240]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":240,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[240]}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4513920}} {"id":"gen-trim-1636","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2648-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1636\nComponents:\n - input (input) params={\"shape\":[1,177]}\n - fc1 (linear) params={\"inFeatures\":177,\"outFeatures\":2648}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2648,\"outFeatures\":2648}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2648,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":177,"outFeatures":301}},{"type":"update_params","name":"fc2","params":{"inFeatures":301,"outFeatures":301}},{"type":"update_params","name":"head","params":{"inFeatures":301,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":153510}} {"id":"gen-norm-1637","family":"norm","seed":20260716,"spec":"This 121-feature, 28-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1637\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":498}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":498,\"outFeatures\":278}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":278,\"outFeatures\":104}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":104,\"outFeatures\":482}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":482,\"outFeatures\":354}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":354,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":498}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":278}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":104}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":482}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":354}}],"grade":{"pass":true,"score":100,"params":458282}} {"id":"gen-tower-1638","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 116-feature user input and a 131-feature item input, each through its own 2-layer MLP tower ending at width 160, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1638\nComponents:\n - user_input (input) params={\"shape\":[1,116]}\n - item_input (input) params={\"shape\":[1,131]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,116]}},{"componentType":"input","name":"item_input","params":{"shape":[1,131]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":116,"outFeatures":160}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":160,"outFeatures":160}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":131,"outFeatures":160}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":160,"outFeatures":160}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":91040}} {"id":"gen-grow-1639","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1639\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":1033}},{"type":"update_params","name":"fc2","params":{"inFeatures":1033,"outFeatures":1033}},{"type":"update_params","name":"head","params":{"inFeatures":1033,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1176587}} {"id":"gen-mlp-1640","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 93-feature input with 40 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1640\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,93]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":93,"outFeatures":683}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":683,"outFeatures":35}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":35,"outFeatures":875}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":875,"outFeatures":614}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":614,"outFeatures":309}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":309,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":857385}} {"id":"gen-ae-1641","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 865-dim input: encode down to a 24-dim bottleneck and decode back to 865. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1641\nComponents:\n - input (input) params={\"shape\":[1,865]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,865]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":865,"outFeatures":798}},{"componentType":"linear","name":"enc2","params":{"inFeatures":798,"outFeatures":24}},{"componentType":"linear","name":"dec1","params":{"inFeatures":24,"outFeatures":798}},{"componentType":"linear","name":"dec2","params":{"inFeatures":798,"outFeatures":865}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1418844}} {"id":"gen-cnn-1642","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 54 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1642\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":59,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":19,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":46143}} {"id":"gen-txf-1643","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 450-token sequences with 75 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1643\nComponents:\n - input (input) params={\"shape\":[1,450]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,450]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36655,"embeddingDim":448}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":448,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":448,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":448,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":18060672}} {"id":"gen-gqa-1644","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 362-token sequences with 50 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1644\nComponents:\n - input (input) params={\"shape\":[1,362]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,362]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33119,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8491264}} {"id":"gen-fix-1645","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (84) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1645\nComponents:\n - input (input) params={\"shape\":[1,77]}\n - embed (embedding) params={\"numEmbeddings\":9880,\"embeddingDim\":84}\n - attn (multiHeadAttention) params={\"embedDim\":84,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":84,\"outFeatures\":97}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":866292}} {"id":"gen-trim-1646","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1623-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1646\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - fc1 (linear) params={\"inFeatures\":157,\"outFeatures\":1623}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1623,\"outFeatures\":1623}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1623,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":157,"outFeatures":120}},{"type":"update_params","name":"fc2","params":{"inFeatures":120,"outFeatures":120}},{"type":"update_params","name":"head","params":{"inFeatures":120,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":36600}} {"id":"gen-norm-1647","family":"norm","seed":20260716,"spec":"This 181-feature, 98-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1647\nComponents:\n - input (input) params={\"shape\":[1,181]}\n - fc1 (linear) params={\"inFeatures\":181,\"outFeatures\":293}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":293,\"outFeatures\":64}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":64,\"outFeatures\":134}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":134,\"outFeatures\":477}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":477,\"outFeatures\":275}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":275,\"outFeatures\":98}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":293}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":64}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":134}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":477}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":275}}],"grade":{"pass":true,"score":100,"params":302404}} {"id":"gen-tower-1648","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 70-feature user input and a 129-feature item input, each through its own 2-layer MLP tower ending at width 384, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1648\nComponents:\n - user_input (input) params={\"shape\":[1,70]}\n - item_input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,70]}},{"componentType":"input","name":"item_input","params":{"shape":[1,129]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":70,"outFeatures":384}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":384,"outFeatures":384}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":129,"outFeatures":384}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":384,"outFeatures":384}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":768,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":372096}} {"id":"gen-grow-1649","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (34-feature input, 13 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1649\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - fc1 (linear) params={\"inFeatures\":34,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":34,"outFeatures":1330}},{"type":"update_params","name":"fc2","params":{"inFeatures":1330,"outFeatures":1330}},{"type":"update_params","name":"head","params":{"inFeatures":1330,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":1831410}} {"id":"gen-mlp-1650","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 161-feature input with 59 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1650\nComponents:\n - input (input) params={\"shape\":[1,161]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,161]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":161,"outFeatures":135}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":135,"outFeatures":899}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":899,"outFeatures":386}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":386,"outFeatures":826}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":826,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":857684}} {"id":"gen-ae-1651","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 840-dim input: encode down to a 149-dim bottleneck and decode back to 840. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1651\nComponents:\n - input (input) params={\"shape\":[1,840]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,840]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":840,"outFeatures":502}},{"componentType":"linear","name":"enc2","params":{"inFeatures":502,"outFeatures":149}},{"componentType":"linear","name":"dec1","params":{"inFeatures":149,"outFeatures":502}},{"componentType":"linear","name":"dec2","params":{"inFeatures":502,"outFeatures":840}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":992956}} {"id":"gen-cnn-1652","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 5 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1652\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":20018}} {"id":"gen-txf-1653","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 126-token sequences with 40 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1653\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,126]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30537,"embeddingDim":192}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":192,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":192,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":192,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":6313152}} {"id":"gen-gqa-1654","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 128-token sequences with 13 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1654\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25680,"embeddingDim":180}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[180]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":180,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[180]}},{"componentType":"linear","name":"head","params":{"inFeatures":180,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4624740}} {"id":"gen-trim-1656","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2855-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1656\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":2855}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2855,\"outFeatures\":2855}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2855,\"outFeatures\":2855}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2855,\"outFeatures\":2855}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2855,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":67,"outFeatures":162}},{"type":"update_params","name":"fc2","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"fc3","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"fc4","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"head","params":{"inFeatures":162,"outFeatures":10}}],"grade":{"pass":true,"score":88,"params":91206}} {"id":"gen-norm-1657","family":"norm","seed":20260716,"spec":"This 89-feature, 5-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1657\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - fc1 (linear) params={\"inFeatures\":89,\"outFeatures\":461}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":461,\"outFeatures\":305}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":305,\"outFeatures\":350}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":350,\"outFeatures\":242}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":242,\"outFeatures\":419}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":419,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":461}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":305}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":350}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":242}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":419}}],"grade":{"pass":true,"score":100,"params":476577}} {"id":"gen-tower-1658","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 152-feature user input and a 199-feature item input, each through its own 2-layer MLP tower ending at width 357, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1658\nComponents:\n - user_input (input) params={\"shape\":[1,152]}\n - item_input (input) params={\"shape\":[1,199]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,152]}},{"componentType":"input","name":"item_input","params":{"shape":[1,199]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":152,"outFeatures":357}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":357,"outFeatures":357}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":199,"outFeatures":357}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":357,"outFeatures":357}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":714,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":380919}} {"id":"gen-grow-1659","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1659\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":1371}},{"type":"update_params","name":"fc2","params":{"inFeatures":1371,"outFeatures":1371}},{"type":"update_params","name":"head","params":{"inFeatures":1371,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":1974240}} {"id":"gen-mlp-1660","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 316-feature input with 12 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1660\nComponents:\n - input (input) params={\"shape\":[1,316]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,316]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":316,"outFeatures":388}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":388,"outFeatures":423}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":423,"outFeatures":89}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":89,"outFeatures":881}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":881,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":413360}} {"id":"gen-ae-1661","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1504-dim input: encode down to a 34-dim bottleneck and decode back to 1504. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1661\nComponents:\n - input (input) params={\"shape\":[1,1504]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1504]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1504,"outFeatures":177}},{"componentType":"linear","name":"enc2","params":{"inFeatures":177,"outFeatures":34}},{"componentType":"linear","name":"dec1","params":{"inFeatures":34,"outFeatures":177}},{"componentType":"linear","name":"dec2","params":{"inFeatures":177,"outFeatures":1504}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":544452}} {"id":"gen-cnn-1662","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 6 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1662\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":51,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":14397}} {"id":"gen-txf-1663","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 78-token sequences with 54 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1663\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,78]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41903,"embeddingDim":20}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":20,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":20,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":842340}} {"id":"gen-gqa-1664","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 128-token sequences with 76 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1664\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19113,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4298336}} {"id":"gen-trim-1666","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3036-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1666\nComponents:\n - input (input) params={\"shape\":[1,218]}\n - fc1 (linear) params={\"inFeatures\":218,\"outFeatures\":3036}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3036,\"outFeatures\":3036}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3036,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":218,"outFeatures":113}},{"type":"update_params","name":"fc2","params":{"inFeatures":113,"outFeatures":113}},{"type":"update_params","name":"head","params":{"inFeatures":113,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":37742}} {"id":"gen-norm-1667","family":"norm","seed":20260716,"spec":"This 199-feature, 31-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1667\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - fc1 (linear) params={\"inFeatures\":199,\"outFeatures\":171}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":171,\"outFeatures\":493}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":493,\"outFeatures\":451}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":451,\"outFeatures\":165}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":165,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":171}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":493}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":165}}],"grade":{"pass":true,"score":100,"params":420205}} {"id":"gen-tower-1668","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 59-feature user input and a 233-feature item input, each through its own 2-layer MLP tower ending at width 235, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1668\nComponents:\n - user_input (input) params={\"shape\":[1,59]}\n - item_input (input) params={\"shape\":[1,233]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,59]}},{"componentType":"input","name":"item_input","params":{"shape":[1,233]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":59,"outFeatures":235}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":235,"outFeatures":235}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":233,"outFeatures":235}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":235,"outFeatures":235}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":470,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":179540}} {"id":"gen-grow-1669","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (59-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1669\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":59,"outFeatures":716}},{"type":"update_params","name":"fc2","params":{"inFeatures":716,"outFeatures":716}},{"type":"update_params","name":"head","params":{"inFeatures":716,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":586404}} {"id":"gen-mlp-1670","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 39-feature input with 199 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1670\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,39]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":39,"outFeatures":411}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":411,"outFeatures":369}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":369,"outFeatures":199}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":241119}} {"id":"gen-ae-1671","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1741-dim input: encode down to a 204-dim bottleneck and decode back to 1741. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1671\nComponents:\n - input (input) params={\"shape\":[1,1741]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1741]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1741,"outFeatures":568}},{"componentType":"linear","name":"enc2","params":{"inFeatures":568,"outFeatures":204}},{"componentType":"linear","name":"dec1","params":{"inFeatures":204,"outFeatures":568}},{"componentType":"linear","name":"dec2","params":{"inFeatures":568,"outFeatures":1741}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2209520}} {"id":"gen-cnn-1672","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 36 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1672\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":29,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":25362}} {"id":"gen-txf-1673","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 284-token sequences with 40 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1673\nComponents:\n - input (input) params={\"shape\":[1,284]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,284]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13640,"embeddingDim":40}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":40,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":566400}} {"id":"gen-gqa-1674","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 407-token sequences with 79 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1674\nComponents:\n - input (input) params={\"shape\":[1,407]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,407]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25135,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5647936}} {"id":"gen-trim-1676","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2671-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1676\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":2671}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2671,\"outFeatures\":2671}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2671,\"outFeatures\":2671}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2671,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":217,"outFeatures":84}},{"type":"update_params","name":"fc2","params":{"inFeatures":84,"outFeatures":84}},{"type":"update_params","name":"fc3","params":{"inFeatures":84,"outFeatures":84}},{"type":"update_params","name":"head","params":{"inFeatures":84,"outFeatures":26}}],"grade":{"pass":true,"score":84,"params":34524}} {"id":"gen-norm-1677","family":"norm","seed":20260716,"spec":"This 138-feature, 85-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1677\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - fc1 (linear) params={\"inFeatures\":138,\"outFeatures\":451}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":451,\"outFeatures\":99}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":99,\"outFeatures\":85}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":99}}],"grade":{"pass":true,"score":94,"params":115302}} {"id":"gen-tower-1678","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 245-feature item input, each through its own 2-layer MLP tower ending at width 82, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1678\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,245]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,245]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":82}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":245,"outFeatures":82}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":164,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":53382}} {"id":"gen-grow-1679","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (55-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1679\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - fc1 (linear) params={\"inFeatures\":55,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":55,"outFeatures":1310}},{"type":"update_params","name":"fc2","params":{"inFeatures":1310,"outFeatures":1310}},{"type":"update_params","name":"head","params":{"inFeatures":1310,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":1792080}} {"id":"gen-mlp-1680","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 293-feature input with 4 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1680\nComponents:\n - input (input) params={\"shape\":[1,293]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,293]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":293,"outFeatures":151}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":151,"outFeatures":609}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":609,"outFeatures":31}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":31,"outFeatures":133}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":133,"outFeatures":471}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":471,"outFeatures":425}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":425,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":423722}} {"id":"gen-ae-1681","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1709-dim input: encode down to a 102-dim bottleneck and decode back to 1709. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1681\nComponents:\n - input (input) params={\"shape\":[1,1709]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1709]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1709,"outFeatures":456}},{"componentType":"linear","name":"enc2","params":{"inFeatures":456,"outFeatures":102}},{"componentType":"linear","name":"dec1","params":{"inFeatures":102,"outFeatures":456}},{"componentType":"linear","name":"dec2","params":{"inFeatures":456,"outFeatures":1709}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1651632}} {"id":"gen-cnn-1682","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 37 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1682\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":51,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":24,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":49,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":24790}} {"id":"gen-txf-1683","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 489-token sequences with 34 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1683\nComponents:\n - input (input) params={\"shape\":[1,489]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,489]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22822,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2463552}} {"id":"gen-gqa-1684","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 445-token sequences with 26 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1684\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24226,"embeddingDim":116}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":116,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[116]}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2813232}} {"id":"gen-trim-1686","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2241-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1686\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":2241}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2241,\"outFeatures\":2241}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2241,\"outFeatures\":2241}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2241,\"outFeatures\":2241}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2241,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":285}},{"type":"update_params","name":"fc2","params":{"inFeatures":285,"outFeatures":285}},{"type":"update_params","name":"fc3","params":{"inFeatures":285,"outFeatures":285}},{"type":"update_params","name":"fc4","params":{"inFeatures":285,"outFeatures":285}},{"type":"update_params","name":"head","params":{"inFeatures":285,"outFeatures":35}}],"grade":{"pass":true,"score":88,"params":262770}} {"id":"gen-norm-1687","family":"norm","seed":20260716,"spec":"This 83-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1687\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":155}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":155,\"outFeatures\":356}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":356,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":155}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":356}}],"grade":{"pass":true,"score":94,"params":99017}} {"id":"gen-tower-1688","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 254-feature user input and a 253-feature item input, each through its own 2-layer MLP tower ending at width 247, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1688\nComponents:\n - user_input (input) params={\"shape\":[1,254]}\n - item_input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,254]}},{"componentType":"input","name":"item_input","params":{"shape":[1,253]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":254,"outFeatures":247}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":247,"outFeatures":247}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":253,"outFeatures":247}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":247,"outFeatures":247}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":494,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":247741}} {"id":"gen-grow-1689","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (124-feature input, 12 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1689\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - fc1 (linear) params={\"inFeatures\":124,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":124,"outFeatures":663}},{"type":"update_params","name":"fc2","params":{"inFeatures":663,"outFeatures":663}},{"type":"update_params","name":"head","params":{"inFeatures":663,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":529737}} {"id":"gen-mlp-1690","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 227-feature input with 122 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1690\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":227,"outFeatures":170}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":170,"outFeatures":899}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":899,"outFeatures":276}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":276,"outFeatures":122}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":473216}} {"id":"gen-ae-1691","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1498-dim input: encode down to a 230-dim bottleneck and decode back to 1498. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1691\nComponents:\n - input (input) params={\"shape\":[1,1498]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1498]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1498,"outFeatures":697}},{"componentType":"linear","name":"enc2","params":{"inFeatures":697,"outFeatures":230}},{"componentType":"linear","name":"dec1","params":{"inFeatures":230,"outFeatures":697}},{"componentType":"linear","name":"dec2","params":{"inFeatures":697,"outFeatures":1498}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2408832}} {"id":"gen-cnn-1692","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 87 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1692\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":42,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":11,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":9756}} {"id":"gen-txf-1693","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 80-token sequences with 68 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1693\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,80]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49098,"embeddingDim":320}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":320,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":320,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":16552320}} {"id":"gen-gqa-1694","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 115-token sequences with 98 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1694\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,115]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27114,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":368,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":10014016}} {"id":"gen-fix-1695","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (78) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1695\nComponents:\n - input (input) params={\"shape\":[1,318]}\n - embed (embedding) params={\"numEmbeddings\":32070,\"embeddingDim\":78}\n - attn (multiHeadAttention) params={\"embedDim\":78,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":78,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2531490}} {"id":"gen-trim-1696","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1745-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1696\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":1745}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1745,\"outFeatures\":1745}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1745,\"outFeatures\":1745}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1745,\"outFeatures\":1745}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1745,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":373}},{"type":"update_params","name":"fc2","params":{"inFeatures":373,"outFeatures":373}},{"type":"update_params","name":"fc3","params":{"inFeatures":373,"outFeatures":373}},{"type":"update_params","name":"fc4","params":{"inFeatures":373,"outFeatures":373}},{"type":"update_params","name":"head","params":{"inFeatures":373,"outFeatures":29}}],"grade":{"pass":true,"score":88,"params":468488}} {"id":"gen-norm-1697","family":"norm","seed":20260716,"spec":"This 217-feature, 41-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1697\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":174}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":174,\"outFeatures\":243}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":243,\"outFeatures\":202}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":202,\"outFeatures\":261}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":261,\"outFeatures\":402}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":402,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":174}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":243}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":202}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":261}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":402}}],"grade":{"pass":true,"score":100,"params":303252}} {"id":"gen-tower-1698","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 103-feature user input and a 54-feature item input, each through its own 2-layer MLP tower ending at width 202, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1698\nComponents:\n - user_input (input) params={\"shape\":[1,103]}\n - item_input (input) params={\"shape\":[1,54]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,103]}},{"componentType":"input","name":"item_input","params":{"shape":[1,54]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":103,"outFeatures":202}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":202,"outFeatures":202}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":54,"outFeatures":202}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":202,"outFeatures":202}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":404,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":113726}} {"id":"gen-grow-1699","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (27-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1699\nComponents:\n - input (input) params={\"shape\":[1,27]}\n - fc1 (linear) params={\"inFeatures\":27,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":27,"outFeatures":665}},{"type":"update_params","name":"fc2","params":{"inFeatures":665,"outFeatures":665}},{"type":"update_params","name":"head","params":{"inFeatures":665,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":465500}} {"id":"gen-mlp-1700","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 319-feature input with 91 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1700\nComponents:\n - input (input) params={\"shape\":[1,319]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,319]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":319,"outFeatures":56}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":56,"outFeatures":756}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":756,"outFeatures":298}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":298,"outFeatures":876}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":876,"outFeatures":1006}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1006,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1519338}} {"id":"gen-ae-1701","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1729-dim input: encode down to a 129-dim bottleneck and decode back to 1729. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1701\nComponents:\n - input (input) params={\"shape\":[1,1729]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1729]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1729,"outFeatures":202}},{"componentType":"linear","name":"enc2","params":{"inFeatures":202,"outFeatures":129}},{"componentType":"linear","name":"dec1","params":{"inFeatures":129,"outFeatures":202}},{"componentType":"linear","name":"dec2","params":{"inFeatures":202,"outFeatures":1729}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":750632}} {"id":"gen-cnn-1702","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 66 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1702\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":28,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":19,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":20829}} {"id":"gen-txf-1703","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 405-token sequences with 18 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1703\nComponents:\n - input (input) params={\"shape\":[1,405]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,405]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39069,"embeddingDim":30}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":30,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":30,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":30,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1183410}} {"id":"gen-gqa-1704","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 343-token sequences with 30 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1704\nComponents:\n - input (input) params={\"shape\":[1,343]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,343]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18048,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":384,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":30}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6941952}} {"id":"gen-fix-1705","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (318) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1705\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - embed (embedding) params={\"numEmbeddings\":40617,\"embeddingDim\":318}\n - attn (multiHeadAttention) params={\"embedDim\":318,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":318,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":13352184}} {"id":"gen-trim-1706","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3966-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1706\nComponents:\n - input (input) params={\"shape\":[1,189]}\n - fc1 (linear) params={\"inFeatures\":189,\"outFeatures\":3966}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3966,\"outFeatures\":3966}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3966,\"outFeatures\":3966}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3966,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":189,"outFeatures":359}},{"type":"update_params","name":"fc2","params":{"inFeatures":359,"outFeatures":359}},{"type":"update_params","name":"fc3","params":{"inFeatures":359,"outFeatures":359}},{"type":"update_params","name":"head","params":{"inFeatures":359,"outFeatures":44}}],"grade":{"pass":true,"score":84,"params":341409}} {"id":"gen-norm-1707","family":"norm","seed":20260716,"spec":"This 38-feature, 55-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1707\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":38}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":38,\"outFeatures\":280}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":280,\"outFeatures\":55}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":38}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":280}}],"grade":{"pass":true,"score":94,"params":27484}} {"id":"gen-tower-1708","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 85-feature user input and a 86-feature item input, each through its own 2-layer MLP tower ending at width 159, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1708\nComponents:\n - user_input (input) params={\"shape\":[1,85]}\n - item_input (input) params={\"shape\":[1,86]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,85]}},{"componentType":"input","name":"item_input","params":{"shape":[1,86]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":85,"outFeatures":159}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":159,"outFeatures":159}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":86,"outFeatures":159}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":159,"outFeatures":159}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":318,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":78069}} {"id":"gen-grow-1709","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (19-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1709\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":19,"outFeatures":1295}},{"type":"update_params","name":"fc2","params":{"inFeatures":1295,"outFeatures":1295}},{"type":"update_params","name":"head","params":{"inFeatures":1295,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":1756020}} {"id":"gen-mlp-1710","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 109-feature input with 35 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1710\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,109]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":109,"outFeatures":912}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":912,"outFeatures":884}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":884,"outFeatures":870}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":870,"outFeatures":845}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":845,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":2439421}} {"id":"gen-ae-1711","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1306-dim input: encode down to a 103-dim bottleneck and decode back to 1306. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1711\nComponents:\n - input (input) params={\"shape\":[1,1306]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1306]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1306,"outFeatures":857}},{"componentType":"linear","name":"enc2","params":{"inFeatures":857,"outFeatures":103}},{"componentType":"linear","name":"dec1","params":{"inFeatures":103,"outFeatures":857}},{"componentType":"linear","name":"dec2","params":{"inFeatures":857,"outFeatures":1306}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2415026}} {"id":"gen-cnn-1712","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 91 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1712\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":51,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15677}} {"id":"gen-txf-1713","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 43-token sequences with 44 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1713\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,43]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29686,"embeddingDim":140}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":140,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":140,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":140,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":140,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4397400}} {"id":"gen-gqa-1714","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 323-token sequences with 54 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1714\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,323]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48024,"embeddingDim":200}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[200]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[200]}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":9615600}} {"id":"gen-trim-1716","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2635-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1716\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - fc1 (linear) params={\"inFeatures\":48,\"outFeatures\":2635}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2635,\"outFeatures\":2635}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2635,\"outFeatures\":2635}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2635,\"outFeatures\":2635}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2635,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":48,"outFeatures":125}},{"type":"update_params","name":"fc2","params":{"inFeatures":125,"outFeatures":125}},{"type":"update_params","name":"fc3","params":{"inFeatures":125,"outFeatures":125}},{"type":"update_params","name":"fc4","params":{"inFeatures":125,"outFeatures":125}},{"type":"update_params","name":"head","params":{"inFeatures":125,"outFeatures":43}}],"grade":{"pass":true,"score":88,"params":58250}} {"id":"gen-norm-1717","family":"norm","seed":20260716,"spec":"This 17-feature, 20-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1717\nComponents:\n - input (input) params={\"shape\":[1,17]}\n - fc1 (linear) params={\"inFeatures\":17,\"outFeatures\":99}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":99,\"outFeatures\":284}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":284,\"outFeatures\":125}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":125,\"outFeatures\":171}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":171,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":99}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":284}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":125}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":171}}],"grade":{"pass":true,"score":100,"params":90094}} {"id":"gen-tower-1718","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 81-feature user input and a 220-feature item input, each through its own 2-layer MLP tower ending at width 303, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1718\nComponents:\n - user_input (input) params={\"shape\":[1,81]}\n - item_input (input) params={\"shape\":[1,220]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,81]}},{"componentType":"input","name":"item_input","params":{"shape":[1,220]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":81,"outFeatures":303}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":303,"outFeatures":303}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":220,"outFeatures":303}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":303,"outFeatures":303}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":606,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":275427}} {"id":"gen-grow-1719","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (65-feature input, 6 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1719\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":1161}},{"type":"update_params","name":"fc2","params":{"inFeatures":1161,"outFeatures":1161}},{"type":"update_params","name":"head","params":{"inFeatures":1161,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":1430352}} {"id":"gen-mlp-1720","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 39-feature input with 8 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1720\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,39]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":39,"outFeatures":363}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":363,"outFeatures":453}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":453,"outFeatures":504}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":504,"outFeatures":346}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":346,"outFeatures":40}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":595452}} {"id":"gen-ae-1721","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1732-dim input: encode down to a 133-dim bottleneck and decode back to 1732. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1721\nComponents:\n - input (input) params={\"shape\":[1,1732]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1732]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1732,"outFeatures":521}},{"componentType":"linear","name":"enc2","params":{"inFeatures":521,"outFeatures":133}},{"componentType":"linear","name":"dec1","params":{"inFeatures":133,"outFeatures":521}},{"componentType":"linear","name":"dec2","params":{"inFeatures":521,"outFeatures":1732}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1943330}} {"id":"gen-cnn-1722","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 25 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1722\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":34,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":51,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":27,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":44729}} {"id":"gen-txf-1723","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 315-token sequences with 78 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1723\nComponents:\n - input (input) params={\"shape\":[1,315]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,315]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13649,"embeddingDim":416}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":416,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":416,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7094880}} {"id":"gen-gqa-1724","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 437-token sequences with 12 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1724\nComponents:\n - input (input) params={\"shape\":[1,437]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,437]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26087,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":136,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[136]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":136,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3549464}} {"id":"gen-trim-1726","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3090-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1726\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - fc1 (linear) params={\"inFeatures\":192,\"outFeatures\":3090}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3090,\"outFeatures\":3090}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3090,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":192,"outFeatures":142}},{"type":"update_params","name":"fc2","params":{"inFeatures":142,"outFeatures":142}},{"type":"update_params","name":"head","params":{"inFeatures":142,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":50694}} {"id":"gen-norm-1727","family":"norm","seed":20260716,"spec":"This 60-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1727\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":98}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":98,\"outFeatures\":235}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":235,\"outFeatures\":301}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":301,\"outFeatures\":38}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":38,\"outFeatures\":169}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":169,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":98}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":235}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":301}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":38}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":169}}],"grade":{"pass":true,"score":100,"params":127645}} {"id":"gen-tower-1728","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 183-feature user input and a 102-feature item input, each through its own 2-layer MLP tower ending at width 435, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1728\nComponents:\n - user_input (input) params={\"shape\":[1,183]}\n - item_input (input) params={\"shape\":[1,102]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,183]}},{"componentType":"input","name":"item_input","params":{"shape":[1,102]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":183,"outFeatures":435}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":435,"outFeatures":435}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":102,"outFeatures":435}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":435,"outFeatures":435}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":870,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":503295}} {"id":"gen-grow-1729","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (122-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1729\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":122,"outFeatures":1083}},{"type":"update_params","name":"fc2","params":{"inFeatures":1083,"outFeatures":1083}},{"type":"update_params","name":"head","params":{"inFeatures":1083,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":1352667}} {"id":"gen-mlp-1730","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 232-feature input with 190 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1730\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,232]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":232,"outFeatures":183}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":183,"outFeatures":138}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":138,"outFeatures":46}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":46,"outFeatures":904}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":904,"outFeatures":657}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":657,"outFeatures":841}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":841,"outFeatures":190}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1421897}} {"id":"gen-ae-1731","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 421-dim input: encode down to a 54-dim bottleneck and decode back to 421. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1731\nComponents:\n - input (input) params={\"shape\":[1,421]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,421]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":421,"outFeatures":840}},{"componentType":"linear","name":"enc2","params":{"inFeatures":840,"outFeatures":54}},{"componentType":"linear","name":"dec1","params":{"inFeatures":54,"outFeatures":840}},{"componentType":"linear","name":"dec2","params":{"inFeatures":840,"outFeatures":421}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":798000}} {"id":"gen-cnn-1732","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 26 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1732\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":13,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":26,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":17910}} {"id":"gen-txf-1733","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 279-token sequences with 83 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1733\nComponents:\n - input (input) params={\"shape\":[1,279]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,279]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37606,"embeddingDim":126}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":126,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":126,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4812318}} {"id":"gen-gqa-1734","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 120-token sequences with 56 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1734\nComponents:\n - input (input) params={\"shape\":[1,120]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,120]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29496,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2836992}} {"id":"gen-trim-1736","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2264-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1736\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - fc1 (linear) params={\"inFeatures\":34,\"outFeatures\":2264}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2264,\"outFeatures\":2264}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2264,\"outFeatures\":2264}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2264,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":34,"outFeatures":135}},{"type":"update_params","name":"fc2","params":{"inFeatures":135,"outFeatures":135}},{"type":"update_params","name":"fc3","params":{"inFeatures":135,"outFeatures":135}},{"type":"update_params","name":"head","params":{"inFeatures":135,"outFeatures":23}}],"grade":{"pass":true,"score":84,"params":44145}} {"id":"gen-norm-1737","family":"norm","seed":20260716,"spec":"This 75-feature, 14-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1737\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":196}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":196,\"outFeatures\":430}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":430,\"outFeatures\":249}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":249,\"outFeatures\":53}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":53,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":196}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":430}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":249}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":53}}],"grade":{"pass":true,"score":100,"params":219989}} {"id":"gen-tower-1738","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 224-feature user input and a 71-feature item input, each through its own 2-layer MLP tower ending at width 483, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1738\nComponents:\n - user_input (input) params={\"shape\":[1,224]}\n - item_input (input) params={\"shape\":[1,71]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,224]}},{"componentType":"input","name":"item_input","params":{"shape":[1,71]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":224,"outFeatures":483}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":483,"outFeatures":483}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":71,"outFeatures":483}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":483,"outFeatures":483}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":966,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":610029}} {"id":"gen-grow-1739","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (45-feature input, 50 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1739\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - fc1 (linear) params={\"inFeatures\":45,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":45,"outFeatures":1022}},{"type":"update_params","name":"fc2","params":{"inFeatures":1022,"outFeatures":1022}},{"type":"update_params","name":"head","params":{"inFeatures":1022,"outFeatures":50}}],"grade":{"pass":true,"score":80,"params":1141574}} {"id":"gen-mlp-1740","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 479-feature input with 173 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1740\nComponents:\n - input (input) params={\"shape\":[1,479]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,479]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":479,"outFeatures":627}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":627,"outFeatures":162}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":162,"outFeatures":264}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":264,"outFeatures":419}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":419,"outFeatures":377}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":377,"outFeatures":455}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":455,"outFeatures":173}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":963504}} {"id":"gen-ae-1741","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1960-dim input: encode down to a 36-dim bottleneck and decode back to 1960. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1741\nComponents:\n - input (input) params={\"shape\":[1,1960]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1960]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1960,"outFeatures":170}},{"componentType":"linear","name":"enc2","params":{"inFeatures":170,"outFeatures":36}},{"componentType":"linear","name":"dec1","params":{"inFeatures":36,"outFeatures":170}},{"componentType":"linear","name":"dec2","params":{"inFeatures":170,"outFeatures":1960}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":678640}} {"id":"gen-cnn-1742","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 31 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1742\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":7338}} {"id":"gen-txf-1743","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 269-token sequences with 19 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1743\nComponents:\n - input (input) params={\"shape\":[1,269]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,269]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9069,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":830720}} {"id":"gen-gqa-1744","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 36-token sequences with 97 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1744\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,36]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15477,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2741024}} {"id":"gen-trim-1746","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3211-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1746\nComponents:\n - input (input) params={\"shape\":[1,246]}\n - fc1 (linear) params={\"inFeatures\":246,\"outFeatures\":3211}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3211,\"outFeatures\":3211}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3211,\"outFeatures\":3211}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3211,\"outFeatures\":3211}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3211,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":246,"outFeatures":220}},{"type":"update_params","name":"fc2","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"fc3","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"fc4","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"head","params":{"inFeatures":220,"outFeatures":11}}],"grade":{"pass":true,"score":88,"params":201740}} {"id":"gen-norm-1747","family":"norm","seed":20260716,"spec":"This 214-feature, 95-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1747\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - fc1 (linear) params={\"inFeatures\":214,\"outFeatures\":414}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":414,\"outFeatures\":166}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":166,\"outFeatures\":66}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":66,\"outFeatures\":95}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":414}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":166}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":66}}],"grade":{"pass":true,"score":100,"params":174546}} {"id":"gen-tower-1748","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 176-feature user input and a 77-feature item input, each through its own 2-layer MLP tower ending at width 499, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1748\nComponents:\n - user_input (input) params={\"shape\":[1,176]}\n - item_input (input) params={\"shape\":[1,77]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,176]}},{"componentType":"input","name":"item_input","params":{"shape":[1,77]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":176,"outFeatures":499}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":499,"outFeatures":499}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":77,"outFeatures":499}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":499,"outFeatures":499}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":998,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":625247}} {"id":"gen-grow-1749","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (20-feature input, 34 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1749\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":20,"outFeatures":755}},{"type":"update_params","name":"fc2","params":{"inFeatures":755,"outFeatures":755}},{"type":"update_params","name":"head","params":{"inFeatures":755,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":610795}} {"id":"gen-mlp-1750","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 445-feature input with 188 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1750\nComponents:\n - input (input) params={\"shape\":[1,445]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,445]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":445,"outFeatures":750}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":750,"outFeatures":256}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":188}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":573878}} {"id":"gen-ae-1751","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 316-dim input: encode down to a 95-dim bottleneck and decode back to 316. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1751\nComponents:\n - input (input) params={\"shape\":[1,316]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,316]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":316,"outFeatures":1016}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1016,"outFeatures":95}},{"componentType":"linear","name":"dec1","params":{"inFeatures":95,"outFeatures":1016}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1016,"outFeatures":316}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":835152}} {"id":"gen-cnn-1752","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 35 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1752\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":55,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":25,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":56,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":42091}} {"id":"gen-txf-1753","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 94-token sequences with 22 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1753\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,94]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17238,"embeddingDim":288}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":288,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":288,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":288,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5966208}} {"id":"gen-gqa-1754","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 69-token sequences with 81 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1754\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,69]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42994,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":560,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":24122000}} {"id":"gen-fix-1755","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (216) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1755\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - embed (embedding) params={\"numEmbeddings\":37138,\"embeddingDim\":216}\n - attn (multiHeadAttention) params={\"embedDim\":216,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":216,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":8215560}} {"id":"gen-trim-1756","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3561-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1756\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - fc1 (linear) params={\"inFeatures\":165,\"outFeatures\":3561}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3561,\"outFeatures\":3561}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3561,\"outFeatures\":3561}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3561,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":165,"outFeatures":237}},{"type":"update_params","name":"fc2","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"fc3","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"head","params":{"inFeatures":237,"outFeatures":9}}],"grade":{"pass":true,"score":84,"params":153576}} {"id":"gen-norm-1757","family":"norm","seed":20260716,"spec":"This 130-feature, 13-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1757\nComponents:\n - input (input) params={\"shape\":[1,130]}\n - fc1 (linear) params={\"inFeatures\":130,\"outFeatures\":97}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":97,\"outFeatures\":75}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":75,\"outFeatures\":44}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":44,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":97}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":75}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":44}}],"grade":{"pass":true,"score":100,"params":23757}} {"id":"gen-tower-1758","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 228-feature user input and a 85-feature item input, each through its own 2-layer MLP tower ending at width 478, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1758\nComponents:\n - user_input (input) params={\"shape\":[1,228]}\n - item_input (input) params={\"shape\":[1,85]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,228]}},{"componentType":"input","name":"item_input","params":{"shape":[1,85]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":228,"outFeatures":478}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":85,"outFeatures":478}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":956,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":607538}} {"id":"gen-grow-1759","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (18-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1759\nComponents:\n - input (input) params={\"shape\":[1,18]}\n - fc1 (linear) params={\"inFeatures\":18,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":18,"outFeatures":979}},{"type":"update_params","name":"fc2","params":{"inFeatures":979,"outFeatures":979}},{"type":"update_params","name":"head","params":{"inFeatures":979,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":1015223}} {"id":"gen-mlp-1760","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 335-feature input with 90 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1760\nComponents:\n - input (input) params={\"shape\":[1,335]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,335]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":335,"outFeatures":800}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":800,"outFeatures":415}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":415,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":637350}} {"id":"gen-ae-1761","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1609-dim input: encode down to a 108-dim bottleneck and decode back to 1609. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1761\nComponents:\n - input (input) params={\"shape\":[1,1609]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1609]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1609,"outFeatures":342}},{"componentType":"linear","name":"enc2","params":{"inFeatures":342,"outFeatures":108}},{"componentType":"linear","name":"dec1","params":{"inFeatures":108,"outFeatures":342}},{"componentType":"linear","name":"dec2","params":{"inFeatures":342,"outFeatures":1609}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1174428}} {"id":"gen-cnn-1762","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 17 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1762\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":17449}} {"id":"gen-txf-1763","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 77-token sequences with 91 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1763\nComponents:\n - input (input) params={\"shape\":[1,77]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,77]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37315,"embeddingDim":448}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":448,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":448,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":448,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":18363520}} {"id":"gen-gqa-1764","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 227-token sequences with 34 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1764\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38036,"embeddingDim":288}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[288]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":288,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[288]}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":10964160}} {"id":"gen-fix-1765","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (208) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1765\nComponents:\n - input (input) params={\"shape\":[1,295]}\n - embed (embedding) params={\"numEmbeddings\":40811,\"embeddingDim\":208}\n - attn (multiHeadAttention) params={\"embedDim\":208,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":208,\"outFeatures\":79}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":8678176}} {"id":"gen-trim-1766","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2861-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1766\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - fc1 (linear) params={\"inFeatures\":236,\"outFeatures\":2861}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2861,\"outFeatures\":2861}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2861,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":236,"outFeatures":319}},{"type":"update_params","name":"fc2","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"head","params":{"inFeatures":319,"outFeatures":9}}],"grade":{"pass":true,"score":80,"params":179916}} {"id":"gen-norm-1767","family":"norm","seed":20260716,"spec":"This 241-feature, 7-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1767\nComponents:\n - input (input) params={\"shape\":[1,241]}\n - fc1 (linear) params={\"inFeatures\":241,\"outFeatures\":371}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":371,\"outFeatures\":353}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":353,\"outFeatures\":185}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":185,\"outFeatures\":180}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":180,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":371}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":353}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":185}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":180}}],"grade":{"pass":true,"score":100,"params":320239}} {"id":"gen-tower-1768","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 182-feature user input and a 144-feature item input, each through its own 2-layer MLP tower ending at width 266, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1768\nComponents:\n - user_input (input) params={\"shape\":[1,182]}\n - item_input (input) params={\"shape\":[1,144]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,182]}},{"componentType":"input","name":"item_input","params":{"shape":[1,144]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":182,"outFeatures":266}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":266,"outFeatures":266}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":144,"outFeatures":266}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":266,"outFeatures":266}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":532,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":228760}} {"id":"gen-grow-1769","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (47-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1769\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":47,"outFeatures":1113}},{"type":"update_params","name":"fc2","params":{"inFeatures":1113,"outFeatures":1113}},{"type":"update_params","name":"head","params":{"inFeatures":1113,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":1330035}} {"id":"gen-mlp-1770","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 192-feature input with 37 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1770\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,192]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":192,"outFeatures":72}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":72,"outFeatures":683}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":683,"outFeatures":288}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":288,"outFeatures":128}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":128,"outFeatures":800}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":800,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":428568}} {"id":"gen-ae-1771","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1861-dim input: encode down to a 183-dim bottleneck and decode back to 1861. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1771\nComponents:\n - input (input) params={\"shape\":[1,1861]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1861]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1861,"outFeatures":710}},{"componentType":"linear","name":"enc2","params":{"inFeatures":710,"outFeatures":183}},{"componentType":"linear","name":"dec1","params":{"inFeatures":183,"outFeatures":710}},{"componentType":"linear","name":"dec2","params":{"inFeatures":710,"outFeatures":1861}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2902480}} {"id":"gen-cnn-1772","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 16 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1772\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":55,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":30056}} {"id":"gen-txf-1773","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 346-token sequences with 6 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1773\nComponents:\n - input (input) params={\"shape\":[1,346]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,346]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19104,"embeddingDim":56}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":56,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1082704}} {"id":"gen-gqa-1774","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 210-token sequences with 77 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1774\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,210]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8691,"embeddingDim":52}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":52,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[52]}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":455936}} {"id":"gen-fix-1775","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (280) is not divisible by numHeads (11). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1775\nComponents:\n - input (input) params={\"shape\":[1,158]}\n - embed (embedding) params={\"numEmbeddings\":12799,\"embeddingDim\":280}\n - attn (multiHeadAttention) params={\"embedDim\":280,\"numHeads\":11}\n - head (linear) params={\"inFeatures\":280,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":3904600}} {"id":"gen-trim-1776","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3260-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1776\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":3260}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3260,\"outFeatures\":3260}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3260,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":66,"outFeatures":361}},{"type":"update_params","name":"fc2","params":{"inFeatures":361,"outFeatures":361}},{"type":"update_params","name":"head","params":{"inFeatures":361,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":156313}} {"id":"gen-norm-1777","family":"norm","seed":20260716,"spec":"This 159-feature, 20-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1777\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":158}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":158,\"outFeatures\":190}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":190,\"outFeatures\":245}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":245,\"outFeatures\":203}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":203,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":158}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":190}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":245}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":203}}],"grade":{"pass":true,"score":100,"params":155487}} {"id":"gen-tower-1778","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 243-feature user input and a 248-feature item input, each through its own 2-layer MLP tower ending at width 190, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1778\nComponents:\n - user_input (input) params={\"shape\":[1,243]}\n - item_input (input) params={\"shape\":[1,248]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,243]}},{"componentType":"input","name":"item_input","params":{"shape":[1,248]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":243,"outFeatures":190}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":190,"outFeatures":190}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":248,"outFeatures":190}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":190,"outFeatures":190}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":380,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":165870}} {"id":"gen-grow-1779","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (85-feature input, 10 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1779\nComponents:\n - input (input) params={\"shape\":[1,85]}\n - fc1 (linear) params={\"inFeatures\":85,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":85,"outFeatures":949}},{"type":"update_params","name":"fc2","params":{"inFeatures":949,"outFeatures":949}},{"type":"update_params","name":"head","params":{"inFeatures":949,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":990756}} {"id":"gen-mlp-1780","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 458-feature input with 114 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1780\nComponents:\n - input (input) params={\"shape\":[1,458]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,458]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":458,"outFeatures":267}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":267,"outFeatures":81}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":81,"outFeatures":470}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":470,"outFeatures":114}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":235563}} {"id":"gen-ae-1781","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1253-dim input: encode down to a 55-dim bottleneck and decode back to 1253. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1781\nComponents:\n - input (input) params={\"shape\":[1,1253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1253]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1253,"outFeatures":553}},{"componentType":"linear","name":"enc2","params":{"inFeatures":553,"outFeatures":55}},{"componentType":"linear","name":"dec1","params":{"inFeatures":55,"outFeatures":553}},{"componentType":"linear","name":"dec2","params":{"inFeatures":553,"outFeatures":1253}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1446648}} {"id":"gen-cnn-1782","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 81 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1782\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":11,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":49770}} {"id":"gen-txf-1783","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 360-token sequences with 31 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1783\nComponents:\n - input (input) params={\"shape\":[1,360]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,360]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32660,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":120,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4038120}} {"id":"gen-gqa-1784","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 110-token sequences with 55 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1784\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,110]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16238,"embeddingDim":264}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":264,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[264]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":264,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[264]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":264,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[264]}},{"componentType":"linear","name":"head","params":{"inFeatures":264,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4301352}} {"id":"gen-trim-1786","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3130-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1786\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":3130}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3130,\"outFeatures\":3130}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3130,\"outFeatures\":3130}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3130,\"outFeatures\":3130}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3130,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":220,"outFeatures":292}},{"type":"update_params","name":"fc2","params":{"inFeatures":292,"outFeatures":292}},{"type":"update_params","name":"fc3","params":{"inFeatures":292,"outFeatures":292}},{"type":"update_params","name":"fc4","params":{"inFeatures":292,"outFeatures":292}},{"type":"update_params","name":"head","params":{"inFeatures":292,"outFeatures":38}}],"grade":{"pass":true,"score":88,"params":331128}} {"id":"gen-norm-1787","family":"norm","seed":20260716,"spec":"This 224-feature, 27-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1787\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - fc1 (linear) params={\"inFeatures\":224,\"outFeatures\":205}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":205,\"outFeatures\":348}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":348,\"outFeatures\":40}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":40,\"outFeatures\":310}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":310,\"outFeatures\":321}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":321,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":205}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":348}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":40}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":310}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":321}}],"grade":{"pass":true,"score":100,"params":251757}} {"id":"gen-tower-1788","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 132-feature user input and a 91-feature item input, each through its own 2-layer MLP tower ending at width 273, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1788\nComponents:\n - user_input (input) params={\"shape\":[1,132]}\n - item_input (input) params={\"shape\":[1,91]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,132]}},{"componentType":"input","name":"item_input","params":{"shape":[1,91]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":132,"outFeatures":273}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":273,"outFeatures":273}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":91,"outFeatures":273}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":273,"outFeatures":273}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":546,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":210483}} {"id":"gen-grow-1789","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1789\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":1236}},{"type":"update_params","name":"fc2","params":{"inFeatures":1236,"outFeatures":1236}},{"type":"update_params","name":"head","params":{"inFeatures":1236,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":1653768}} {"id":"gen-mlp-1790","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 104-feature input with 18 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1790\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,104]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":104,"outFeatures":686}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":686,"outFeatures":733}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":733,"outFeatures":791}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":791,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1168223}} {"id":"gen-ae-1791","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2007-dim input: encode down to a 148-dim bottleneck and decode back to 2007. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1791\nComponents:\n - input (input) params={\"shape\":[1,2007]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2007]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2007,"outFeatures":509}},{"componentType":"linear","name":"enc2","params":{"inFeatures":509,"outFeatures":148}},{"componentType":"linear","name":"dec1","params":{"inFeatures":148,"outFeatures":509}},{"componentType":"linear","name":"dec2","params":{"inFeatures":509,"outFeatures":2007}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2193790}} {"id":"gen-cnn-1792","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 72 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1792\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":48,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":21,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":58,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":27,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37368}} {"id":"gen-txf-1793","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 427-token sequences with 68 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1793\nComponents:\n - input (input) params={\"shape\":[1,427]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,427]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48033,"embeddingDim":76}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":76,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":76,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3701884}} {"id":"gen-gqa-1794","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 227-token sequences with 3 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1794\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31780,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":12713200}} {"id":"gen-fix-1795","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (294) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1795\nComponents:\n - input (input) params={\"shape\":[1,269]}\n - embed (embedding) params={\"numEmbeddings\":48220,\"embeddingDim\":294}\n - attn (multiHeadAttention) params={\"embedDim\":294,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":294,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":14524482}} {"id":"gen-trim-1796","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3655-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1796\nComponents:\n - input (input) params={\"shape\":[1,85]}\n - fc1 (linear) params={\"inFeatures\":85,\"outFeatures\":3655}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3655,\"outFeatures\":3655}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3655,\"outFeatures\":3655}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3655,\"outFeatures\":3655}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3655,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":85,"outFeatures":175}},{"type":"update_params","name":"fc2","params":{"inFeatures":175,"outFeatures":175}},{"type":"update_params","name":"fc3","params":{"inFeatures":175,"outFeatures":175}},{"type":"update_params","name":"fc4","params":{"inFeatures":175,"outFeatures":175}},{"type":"update_params","name":"head","params":{"inFeatures":175,"outFeatures":37}}],"grade":{"pass":true,"score":88,"params":113225}} {"id":"gen-norm-1797","family":"norm","seed":20260716,"spec":"This 123-feature, 41-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1797\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - fc1 (linear) params={\"inFeatures\":123,\"outFeatures\":115}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":115,\"outFeatures\":397}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":397,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":115}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":397}}],"grade":{"pass":true,"score":94,"params":76077}} {"id":"gen-tower-1798","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 204-feature user input and a 74-feature item input, each through its own 2-layer MLP tower ending at width 33, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1798\nComponents:\n - user_input (input) params={\"shape\":[1,204]}\n - item_input (input) params={\"shape\":[1,74]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,204]}},{"componentType":"input","name":"item_input","params":{"shape":[1,74]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":204,"outFeatures":33}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":33,"outFeatures":33}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":74,"outFeatures":33}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":33,"outFeatures":33}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":66,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":11418}} {"id":"gen-grow-1799","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (25-feature input, 17 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1799\nComponents:\n - input (input) params={\"shape\":[1,25]}\n - fc1 (linear) params={\"inFeatures\":25,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":25,"outFeatures":798}},{"type":"update_params","name":"fc2","params":{"inFeatures":798,"outFeatures":798}},{"type":"update_params","name":"head","params":{"inFeatures":798,"outFeatures":17}}],"grade":{"pass":true,"score":80,"params":670320}} {"id":"gen-mlp-1800","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 244-feature input with 25 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1800\nComponents:\n - input (input) params={\"shape\":[1,244]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,244]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":244,"outFeatures":220}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":220,"outFeatures":188}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":188,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":99740}} {"id":"gen-ae-1801","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1801-dim input: encode down to a 245-dim bottleneck and decode back to 1801. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1801\nComponents:\n - input (input) params={\"shape\":[1,1801]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1801]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1801,"outFeatures":425}},{"componentType":"linear","name":"enc2","params":{"inFeatures":425,"outFeatures":245}},{"componentType":"linear","name":"dec1","params":{"inFeatures":245,"outFeatures":425}},{"componentType":"linear","name":"dec2","params":{"inFeatures":425,"outFeatures":1801}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1739100}} {"id":"gen-cnn-1802","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 87 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1802\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":6750}} {"id":"gen-txf-1803","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 486-token sequences with 21 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1803\nComponents:\n - input (input) params={\"shape\":[1,486]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,486]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36454,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":120,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4492200}} {"id":"gen-gqa-1804","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 276-token sequences with 25 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1804\nComponents:\n - input (input) params={\"shape\":[1,276]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,276]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46486,"embeddingDim":704}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":704,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[704]}},{"componentType":"linear","name":"head","params":{"inFeatures":704,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":32743744}} {"id":"gen-trim-1806","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1747-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1806\nComponents:\n - input (input) params={\"shape\":[1,255]}\n - fc1 (linear) params={\"inFeatures\":255,\"outFeatures\":1747}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1747,\"outFeatures\":1747}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1747,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":255,"outFeatures":126}},{"type":"update_params","name":"fc2","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"head","params":{"inFeatures":126,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":50400}} {"id":"gen-norm-1807","family":"norm","seed":20260716,"spec":"This 49-feature, 8-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1807\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":263}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":263,\"outFeatures\":293}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":293,\"outFeatures\":305}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":305,\"outFeatures\":165}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":165,\"outFeatures\":179}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":179,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":263}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":293}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":305}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":165}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":179}}],"grade":{"pass":true,"score":100,"params":260603}} {"id":"gen-tower-1808","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 253-feature user input and a 84-feature item input, each through its own 2-layer MLP tower ending at width 405, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1808\nComponents:\n - user_input (input) params={\"shape\":[1,253]}\n - item_input (input) params={\"shape\":[1,84]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,253]}},{"componentType":"input","name":"item_input","params":{"shape":[1,84]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":253,"outFeatures":405}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":405,"outFeatures":405}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":84,"outFeatures":405}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":405,"outFeatures":405}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":810,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":465345}} {"id":"gen-grow-1809","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (91-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1809\nComponents:\n - input (input) params={\"shape\":[1,91]}\n - fc1 (linear) params={\"inFeatures\":91,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":91,"outFeatures":1213}},{"type":"update_params","name":"fc2","params":{"inFeatures":1213,"outFeatures":1213}},{"type":"update_params","name":"head","params":{"inFeatures":1213,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1633911}} {"id":"gen-mlp-1810","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 442-feature input with 172 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1810\nComponents:\n - input (input) params={\"shape\":[1,442]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,442]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":442,"outFeatures":676}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":676,"outFeatures":855}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":855,"outFeatures":224}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":172}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1106820}} {"id":"gen-ae-1811","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 985-dim input: encode down to a 49-dim bottleneck and decode back to 985. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1811\nComponents:\n - input (input) params={\"shape\":[1,985]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,985]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":985,"outFeatures":484}},{"componentType":"linear","name":"enc2","params":{"inFeatures":484,"outFeatures":49}},{"componentType":"linear","name":"dec1","params":{"inFeatures":49,"outFeatures":484}},{"componentType":"linear","name":"dec2","params":{"inFeatures":484,"outFeatures":985}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1000912}} {"id":"gen-cnn-1812","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 47 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1812\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":38,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":29493}} {"id":"gen-txf-1813","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 228-token sequences with 29 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1813\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30959,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6357600}} {"id":"gen-gqa-1814","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 395-token sequences with 23 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1814\nComponents:\n - input (input) params={\"shape\":[1,395]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,395]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14274,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":336,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4803792}} {"id":"gen-trim-1816","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 4079-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1816\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":4079}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4079,\"outFeatures\":4079}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4079,\"outFeatures\":4079}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":4079,\"outFeatures\":4079}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":4079,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":159,"outFeatures":275}},{"type":"update_params","name":"fc2","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"fc3","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"fc4","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"head","params":{"inFeatures":275,"outFeatures":8}}],"grade":{"pass":true,"score":88,"params":272800}} {"id":"gen-norm-1817","family":"norm","seed":20260716,"spec":"This 126-feature, 39-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1817\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":67}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":67,\"outFeatures\":49}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":49,\"outFeatures\":138}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":138,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":67}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":49}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":138}}],"grade":{"pass":true,"score":100,"params":23869}} {"id":"gen-tower-1818","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 22-feature user input and a 254-feature item input, each through its own 2-layer MLP tower ending at width 373, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1818\nComponents:\n - user_input (input) params={\"shape\":[1,22]}\n - item_input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,22]}},{"componentType":"input","name":"item_input","params":{"shape":[1,254]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":22,"outFeatures":373}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":373,"outFeatures":373}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":254,"outFeatures":373}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":373,"outFeatures":373}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":746,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":381952}} {"id":"gen-grow-1819","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (47-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1819\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":47,"outFeatures":1013}},{"type":"update_params","name":"fc2","params":{"inFeatures":1013,"outFeatures":1013}},{"type":"update_params","name":"head","params":{"inFeatures":1013,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1095053}} {"id":"gen-mlp-1820","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 296-feature input with 69 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1820\nComponents:\n - input (input) params={\"shape\":[1,296]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,296]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":296,"outFeatures":615}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":615,"outFeatures":243}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":243,"outFeatures":294}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":294,"outFeatures":705}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":705,"outFeatures":75}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":75,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":668247}} {"id":"gen-ae-1821","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 558-dim input: encode down to a 174-dim bottleneck and decode back to 558. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1821\nComponents:\n - input (input) params={\"shape\":[1,558]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,558]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":558,"outFeatures":293}},{"componentType":"linear","name":"enc2","params":{"inFeatures":293,"outFeatures":174}},{"componentType":"linear","name":"dec1","params":{"inFeatures":174,"outFeatures":293}},{"componentType":"linear","name":"dec2","params":{"inFeatures":293,"outFeatures":558}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":428952}} {"id":"gen-cnn-1822","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 86 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1822\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":41,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":52113}} {"id":"gen-txf-1823","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 494-token sequences with 77 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1823\nComponents:\n - input (input) params={\"shape\":[1,494]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,494]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36164,"embeddingDim":20}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":20,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":20,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":20,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":729620}} {"id":"gen-gqa-1824","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 86-token sequences with 50 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1824\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,86]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30616,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4906560}} {"id":"gen-trim-1826","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3589-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1826\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - fc1 (linear) params={\"inFeatures\":170,\"outFeatures\":3589}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3589,\"outFeatures\":3589}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3589,\"outFeatures\":3589}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3589,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":170,"outFeatures":316}},{"type":"update_params","name":"fc2","params":{"inFeatures":316,"outFeatures":316}},{"type":"update_params","name":"fc3","params":{"inFeatures":316,"outFeatures":316}},{"type":"update_params","name":"head","params":{"inFeatures":316,"outFeatures":15}}],"grade":{"pass":true,"score":84,"params":258172}} {"id":"gen-norm-1827","family":"norm","seed":20260716,"spec":"This 90-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1827\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":39}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":39,\"outFeatures\":150}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":150,\"outFeatures\":34}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":34,\"outFeatures\":432}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":432,\"outFeatures\":134}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":134,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":39}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":150}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":34}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":432}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":134}}],"grade":{"pass":true,"score":100,"params":91458}} {"id":"gen-tower-1828","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 219-feature user input and a 68-feature item input, each through its own 2-layer MLP tower ending at width 351, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1828\nComponents:\n - user_input (input) params={\"shape\":[1,219]}\n - item_input (input) params={\"shape\":[1,68]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,219]}},{"componentType":"input","name":"item_input","params":{"shape":[1,68]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":219,"outFeatures":351}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":351,"outFeatures":351}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":68,"outFeatures":351}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":351,"outFeatures":351}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":702,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":347841}} {"id":"gen-grow-1829","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (32-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1829\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":1118}},{"type":"update_params","name":"fc2","params":{"inFeatures":1118,"outFeatures":1118}},{"type":"update_params","name":"head","params":{"inFeatures":1118,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":1329302}} {"id":"gen-mlp-1830","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 129-feature input with 193 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1830\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,129]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":129,"outFeatures":284}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":284,"outFeatures":384}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":384,"outFeatures":75}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":75,"outFeatures":637}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":637,"outFeatures":234}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":234,"outFeatures":301}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":301,"outFeatures":193}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":499852}} {"id":"gen-ae-1831","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 320-dim input: encode down to a 96-dim bottleneck and decode back to 320. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1831\nComponents:\n - input (input) params={\"shape\":[1,320]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,320]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":320,"outFeatures":956}},{"componentType":"linear","name":"enc2","params":{"inFeatures":956,"outFeatures":96}},{"componentType":"linear","name":"dec1","params":{"inFeatures":96,"outFeatures":956}},{"componentType":"linear","name":"dec2","params":{"inFeatures":956,"outFeatures":320}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":795392}} {"id":"gen-cnn-1832","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 51 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1832\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":30,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":13,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12384}} {"id":"gen-txf-1833","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 245-token sequences with 57 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1833\nComponents:\n - input (input) params={\"shape\":[1,245]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,245]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11819,"embeddingDim":96}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":96,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1176960}} {"id":"gen-gqa-1834","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 107-token sequences with 58 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1834\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,107]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25245,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":9716352}} {"id":"gen-trim-1836","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3575-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1836\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":3575}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3575,\"outFeatures\":3575}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3575,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":107,"outFeatures":99}},{"type":"update_params","name":"fc2","params":{"inFeatures":99,"outFeatures":99}},{"type":"update_params","name":"head","params":{"inFeatures":99,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":23562}} {"id":"gen-norm-1837","family":"norm","seed":20260716,"spec":"This 167-feature, 37-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1837\nComponents:\n - input (input) params={\"shape\":[1,167]}\n - fc1 (linear) params={\"inFeatures\":167,\"outFeatures\":464}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":464,\"outFeatures\":220}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":220,\"outFeatures\":160}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":160,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":464}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":220}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":160}}],"grade":{"pass":true,"score":100,"params":220688}} {"id":"gen-tower-1838","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 131-feature user input and a 203-feature item input, each through its own 2-layer MLP tower ending at width 374, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1838\nComponents:\n - user_input (input) params={\"shape\":[1,131]}\n - item_input (input) params={\"shape\":[1,203]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,131]}},{"componentType":"input","name":"item_input","params":{"shape":[1,203]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":131,"outFeatures":374}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":374,"outFeatures":374}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":203,"outFeatures":374}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":374,"outFeatures":374}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":748,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":405416}} {"id":"gen-grow-1839","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (58-feature input, 50 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1839\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - fc1 (linear) params={\"inFeatures\":58,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":58,"outFeatures":648}},{"type":"update_params","name":"fc2","params":{"inFeatures":648,"outFeatures":648}},{"type":"update_params","name":"head","params":{"inFeatures":648,"outFeatures":50}}],"grade":{"pass":true,"score":80,"params":489888}} {"id":"gen-mlp-1840","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 34-feature input with 88 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1840\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,34]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":34,"outFeatures":475}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":475,"outFeatures":309}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":309,"outFeatures":357}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":357,"outFeatures":215}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":215,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":368913}} {"id":"gen-ae-1841","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 214-dim input: encode down to a 187-dim bottleneck and decode back to 214. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1841\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,214]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":214,"outFeatures":204}},{"componentType":"linear","name":"enc2","params":{"inFeatures":204,"outFeatures":187}},{"componentType":"linear","name":"dec1","params":{"inFeatures":187,"outFeatures":204}},{"componentType":"linear","name":"dec2","params":{"inFeatures":204,"outFeatures":214}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":163608}} {"id":"gen-cnn-1842","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 43 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1842\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":62,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":36974}} {"id":"gen-txf-1843","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 417-token sequences with 26 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1843\nComponents:\n - input (input) params={\"shape\":[1,417]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,417]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12488,"embeddingDim":216}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":216,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":216,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3076272}} {"id":"gen-gqa-1844","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 369-token sequences with 57 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1844\nComponents:\n - input (input) params={\"shape\":[1,369]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,369]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40586,"embeddingDim":120}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":120,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[120]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":120,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[120]}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4877160}} {"id":"gen-trim-1846","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3214-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1846\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - fc1 (linear) params={\"inFeatures\":134,\"outFeatures\":3214}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3214,\"outFeatures\":3214}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3214,\"outFeatures\":3214}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3214,\"outFeatures\":3214}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3214,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":134,"outFeatures":187}},{"type":"update_params","name":"fc2","params":{"inFeatures":187,"outFeatures":187}},{"type":"update_params","name":"fc3","params":{"inFeatures":187,"outFeatures":187}},{"type":"update_params","name":"fc4","params":{"inFeatures":187,"outFeatures":187}},{"type":"update_params","name":"head","params":{"inFeatures":187,"outFeatures":36}}],"grade":{"pass":true,"score":88,"params":136697}} {"id":"gen-norm-1847","family":"norm","seed":20260716,"spec":"This 118-feature, 86-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1847\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":344}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":344,\"outFeatures\":155}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":155,\"outFeatures\":192}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":192,\"outFeatures\":316}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":316,\"outFeatures\":413}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":413,\"outFeatures\":86}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":344}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":155}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":192}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":316}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":413}}],"grade":{"pass":true,"score":100,"params":350370}} {"id":"gen-tower-1848","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 29-feature user input and a 230-feature item input, each through its own 2-layer MLP tower ending at width 358, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1848\nComponents:\n - user_input (input) params={\"shape\":[1,29]}\n - item_input (input) params={\"shape\":[1,230]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,29]}},{"componentType":"input","name":"item_input","params":{"shape":[1,230]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":29,"outFeatures":358}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":358,"outFeatures":358}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":230,"outFeatures":358}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":358,"outFeatures":358}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":716,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":349766}} {"id":"gen-grow-1849","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (65-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1849\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":1173}},{"type":"update_params","name":"fc2","params":{"inFeatures":1173,"outFeatures":1173}},{"type":"update_params","name":"head","params":{"inFeatures":1173,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1483845}} {"id":"gen-mlp-1850","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 108-feature input with 148 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1850\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,108]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":108,"outFeatures":911}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":911,"outFeatures":126}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":126,"outFeatures":827}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":827,"outFeatures":148}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":439772}} {"id":"gen-ae-1851","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 322-dim input: encode down to a 130-dim bottleneck and decode back to 322. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1851\nComponents:\n - input (input) params={\"shape\":[1,322]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,322]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":322,"outFeatures":67}},{"componentType":"linear","name":"enc2","params":{"inFeatures":67,"outFeatures":130}},{"componentType":"linear","name":"dec1","params":{"inFeatures":130,"outFeatures":67}},{"componentType":"linear","name":"dec2","params":{"inFeatures":67,"outFeatures":322}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":60568}} {"id":"gen-cnn-1852","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 96 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1852\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":32352}} {"id":"gen-txf-1853","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 278-token sequences with 87 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1853\nComponents:\n - input (input) params={\"shape\":[1,278]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,278]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43284,"embeddingDim":376}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":376,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":16873000}} {"id":"gen-gqa-1854","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 96-token sequences with 80 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1854\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,96]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19496,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":128,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[128]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":128,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2505728}} {"id":"gen-trim-1856","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2137-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1856\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":2137}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2137,\"outFeatures\":2137}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2137,\"outFeatures\":2137}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2137,\"outFeatures\":2137}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2137,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":64,"outFeatures":164}},{"type":"update_params","name":"fc2","params":{"inFeatures":164,"outFeatures":164}},{"type":"update_params","name":"fc3","params":{"inFeatures":164,"outFeatures":164}},{"type":"update_params","name":"fc4","params":{"inFeatures":164,"outFeatures":164}},{"type":"update_params","name":"head","params":{"inFeatures":164,"outFeatures":44}}],"grade":{"pass":true,"score":88,"params":98400}} {"id":"gen-norm-1857","family":"norm","seed":20260716,"spec":"This 217-feature, 4-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1857\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":39}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":39,\"outFeatures\":41}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":41,\"outFeatures\":111}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":111,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":39}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":41}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":111}}],"grade":{"pass":true,"score":100,"params":15057}} {"id":"gen-tower-1858","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 25-feature user input and a 171-feature item input, each through its own 2-layer MLP tower ending at width 205, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1858\nComponents:\n - user_input (input) params={\"shape\":[1,25]}\n - item_input (input) params={\"shape\":[1,171]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,25]}},{"componentType":"input","name":"item_input","params":{"shape":[1,171]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":25,"outFeatures":205}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":205,"outFeatures":205}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":171,"outFeatures":205}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":205,"outFeatures":205}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":410,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":124640}} {"id":"gen-grow-1859","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (25-feature input, 44 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1859\nComponents:\n - input (input) params={\"shape\":[1,25]}\n - fc1 (linear) params={\"inFeatures\":25,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":25,"outFeatures":1015}},{"type":"update_params","name":"fc2","params":{"inFeatures":1015,"outFeatures":1015}},{"type":"update_params","name":"head","params":{"inFeatures":1015,"outFeatures":44}}],"grade":{"pass":true,"score":80,"params":1100260}} {"id":"gen-mlp-1860","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 287-feature input with 136 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1860\nComponents:\n - input (input) params={\"shape\":[1,287]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,287]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":287,"outFeatures":247}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":247,"outFeatures":579}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":579,"outFeatures":495}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":495,"outFeatures":326}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":326,"outFeatures":136}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":706213}} {"id":"gen-ae-1861","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 998-dim input: encode down to a 36-dim bottleneck and decode back to 998. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1861\nComponents:\n - input (input) params={\"shape\":[1,998]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,998]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":998,"outFeatures":953}},{"componentType":"linear","name":"enc2","params":{"inFeatures":953,"outFeatures":36}},{"componentType":"linear","name":"dec1","params":{"inFeatures":36,"outFeatures":953}},{"componentType":"linear","name":"dec2","params":{"inFeatures":953,"outFeatures":998}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1970804}} {"id":"gen-cnn-1862","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 84 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1862\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":41,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":84}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":40773}} {"id":"gen-txf-1863","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 42-token sequences with 4 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1863\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,42]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46310,"embeddingDim":136}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":136,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":136,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":136,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":6520656}} {"id":"gen-gqa-1864","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 451-token sequences with 55 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1864\nComponents:\n - input (input) params={\"shape\":[1,451]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,451]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25173,"embeddingDim":84}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":84,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[84]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":84,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[84]}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2119152}} {"id":"gen-trim-1866","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3575-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1866\nComponents:\n - input (input) params={\"shape\":[1,211]}\n - fc1 (linear) params={\"inFeatures\":211,\"outFeatures\":3575}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3575,\"outFeatures\":3575}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3575,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":211,"outFeatures":188}},{"type":"update_params","name":"fc2","params":{"inFeatures":188,"outFeatures":188}},{"type":"update_params","name":"head","params":{"inFeatures":188,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":84036}} {"id":"gen-norm-1867","family":"norm","seed":20260716,"spec":"This 38-feature, 73-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1867\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":389}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":389,\"outFeatures\":53}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":53,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":389}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":53}}],"grade":{"pass":true,"score":94,"params":39268}} {"id":"gen-tower-1868","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 56-feature user input and a 253-feature item input, each through its own 2-layer MLP tower ending at width 384, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1868\nComponents:\n - user_input (input) params={\"shape\":[1,56]}\n - item_input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,56]}},{"componentType":"input","name":"item_input","params":{"shape":[1,253]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":56,"outFeatures":384}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":384,"outFeatures":384}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":253,"outFeatures":384}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":384,"outFeatures":384}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":768,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":414336}} {"id":"gen-grow-1869","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (55-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1869\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - fc1 (linear) params={\"inFeatures\":55,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":55,"outFeatures":957}},{"type":"update_params","name":"fc2","params":{"inFeatures":957,"outFeatures":957}},{"type":"update_params","name":"head","params":{"inFeatures":957,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":995280}} {"id":"gen-mlp-1870","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 80-feature input with 169 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1870\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,80]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":80,"outFeatures":467}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":467,"outFeatures":591}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":591,"outFeatures":114}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":114,"outFeatures":408}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":408,"outFeatures":496}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":496,"outFeatures":678}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":678,"outFeatures":169}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1080481}} {"id":"gen-ae-1871","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1471-dim input: encode down to a 105-dim bottleneck and decode back to 1471. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1871\nComponents:\n - input (input) params={\"shape\":[1,1471]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1471]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1471,"outFeatures":878}},{"componentType":"linear","name":"enc2","params":{"inFeatures":878,"outFeatures":105}},{"componentType":"linear","name":"dec1","params":{"inFeatures":105,"outFeatures":878}},{"componentType":"linear","name":"dec2","params":{"inFeatures":878,"outFeatures":1471}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2767456}} {"id":"gen-cnn-1872","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x62x62 image with 53 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1872\nComponents:\n - input (input) params={\"shape\":[1,3,62,62]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,62,62]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":36,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":32929}} {"id":"gen-txf-1873","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 70-token sequences with 76 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1873\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,70]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40244,"embeddingDim":160}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":160,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6553600}} {"id":"gen-gqa-1874","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 365-token sequences with 62 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1874\nComponents:\n - input (input) params={\"shape\":[1,365]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,365]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12847,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8055216}} {"id":"gen-fix-1875","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (330) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1875\nComponents:\n - input (input) params={\"shape\":[1,427]}\n - embed (embedding) params={\"numEmbeddings\":32799,\"embeddingDim\":330}\n - attn (multiHeadAttention) params={\"embedDim\":330,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":330,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":11272470}} {"id":"gen-trim-1876","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1829-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1876\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":1829}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1829,\"outFeatures\":1829}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1829,\"outFeatures\":1829}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1829,\"outFeatures\":1829}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1829,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":189}},{"type":"update_params","name":"fc2","params":{"inFeatures":189,"outFeatures":189}},{"type":"update_params","name":"fc3","params":{"inFeatures":189,"outFeatures":189}},{"type":"update_params","name":"fc4","params":{"inFeatures":189,"outFeatures":189}},{"type":"update_params","name":"head","params":{"inFeatures":189,"outFeatures":14}}],"grade":{"pass":true,"score":88,"params":124173}} {"id":"gen-norm-1877","family":"norm","seed":20260716,"spec":"This 157-feature, 14-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1877\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - fc1 (linear) params={\"inFeatures\":157,\"outFeatures\":93}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":93,\"outFeatures\":398}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":398,\"outFeatures\":389}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":389,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":93}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":398}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":389}}],"grade":{"pass":true,"score":100,"params":211883}} {"id":"gen-tower-1878","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 101-feature user input and a 245-feature item input, each through its own 2-layer MLP tower ending at width 38, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1878\nComponents:\n - user_input (input) params={\"shape\":[1,101]}\n - item_input (input) params={\"shape\":[1,245]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,101]}},{"componentType":"input","name":"item_input","params":{"shape":[1,245]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":101,"outFeatures":38}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":38,"outFeatures":38}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":245,"outFeatures":38}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":38,"outFeatures":38}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":16112}} {"id":"gen-grow-1879","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (54-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1879\nComponents:\n - input (input) params={\"shape\":[1,54]}\n - fc1 (linear) params={\"inFeatures\":54,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":54,"outFeatures":1205}},{"type":"update_params","name":"fc2","params":{"inFeatures":1205,"outFeatures":1205}},{"type":"update_params","name":"head","params":{"inFeatures":1205,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":1541195}} {"id":"gen-mlp-1880","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 208-feature input with 72 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1880\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,208]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":208,"outFeatures":560}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":560,"outFeatures":947}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":947,"outFeatures":53}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":53,"outFeatures":992}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":992,"outFeatures":394}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":394,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1168783}} {"id":"gen-ae-1881","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1724-dim input: encode down to a 173-dim bottleneck and decode back to 1724. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1881\nComponents:\n - input (input) params={\"shape\":[1,1724]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1724]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1724,"outFeatures":770}},{"componentType":"linear","name":"enc2","params":{"inFeatures":770,"outFeatures":173}},{"componentType":"linear","name":"dec1","params":{"inFeatures":173,"outFeatures":770}},{"componentType":"linear","name":"dec2","params":{"inFeatures":770,"outFeatures":1724}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2921380}} {"id":"gen-cnn-1882","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 49 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1882\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":8503}} {"id":"gen-txf-1883","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 353-token sequences with 89 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1883\nComponents:\n - input (input) params={\"shape\":[1,353]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,353]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47884,"embeddingDim":256}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":256,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":256,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":256,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":13067520}} {"id":"gen-gqa-1884","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 244-token sequences with 38 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1884\nComponents:\n - input (input) params={\"shape\":[1,244]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,244]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23476,"embeddingDim":100}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[100]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":100,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[100]}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2351400}} {"id":"gen-fix-1885","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (358) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1885\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - embed (embedding) params={\"numEmbeddings\":29999,\"embeddingDim\":358}\n - attn (multiHeadAttention) params={\"embedDim\":358,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":358,\"outFeatures\":88}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":11283802}} {"id":"gen-trim-1886","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3624-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1886\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - fc1 (linear) params={\"inFeatures\":119,\"outFeatures\":3624}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3624,\"outFeatures\":3624}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3624,\"outFeatures\":3624}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3624,\"outFeatures\":3624}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3624,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":119,"outFeatures":297}},{"type":"update_params","name":"fc2","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"fc3","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"fc4","params":{"inFeatures":297,"outFeatures":297}},{"type":"update_params","name":"head","params":{"inFeatures":297,"outFeatures":46}}],"grade":{"pass":true,"score":88,"params":313632}} {"id":"gen-norm-1887","family":"norm","seed":20260716,"spec":"This 111-feature, 28-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1887\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - fc1 (linear) params={\"inFeatures\":111,\"outFeatures\":483}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":483,\"outFeatures\":192}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":192,\"outFeatures\":409}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":409,\"outFeatures\":455}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":455,\"outFeatures\":57}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":57,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":483}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":192}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":409}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":455}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":57}}],"grade":{"pass":true,"score":100,"params":438503}} {"id":"gen-tower-1888","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 94-feature user input and a 17-feature item input, each through its own 2-layer MLP tower ending at width 407, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1888\nComponents:\n - user_input (input) params={\"shape\":[1,94]}\n - item_input (input) params={\"shape\":[1,17]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,94]}},{"componentType":"input","name":"item_input","params":{"shape":[1,17]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":94,"outFeatures":407}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":407,"outFeatures":407}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":17,"outFeatures":407}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":407,"outFeatures":407}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":814,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":377289}} {"id":"gen-grow-1889","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (98-feature input, 13 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1889\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":98,"outFeatures":1395}},{"type":"update_params","name":"fc2","params":{"inFeatures":1395,"outFeatures":1395}},{"type":"update_params","name":"head","params":{"inFeatures":1395,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":2100870}} {"id":"gen-mlp-1890","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 124-feature input with 114 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1890\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,124]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":124,"outFeatures":630}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":630,"outFeatures":581}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":581,"outFeatures":702}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":702,"outFeatures":218}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":218,"outFeatures":114}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1029900}} {"id":"gen-ae-1891","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1220-dim input: encode down to a 134-dim bottleneck and decode back to 1220. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1891\nComponents:\n - input (input) params={\"shape\":[1,1220]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1220]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1220,"outFeatures":227}},{"componentType":"linear","name":"enc2","params":{"inFeatures":227,"outFeatures":134}},{"componentType":"linear","name":"dec1","params":{"inFeatures":134,"outFeatures":227}},{"componentType":"linear","name":"dec2","params":{"inFeatures":227,"outFeatures":1220}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":614716}} {"id":"gen-cnn-1892","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 62 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1892\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":13158}} {"id":"gen-txf-1893","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 336-token sequences with 27 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1893\nComponents:\n - input (input) params={\"shape\":[1,336]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,336]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21251,"embeddingDim":360}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":360,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":360,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":8696880}} {"id":"gen-gqa-1894","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 124-token sequences with 39 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1894\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,124]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38012,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8523424}} {"id":"gen-trim-1896","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3610-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1896\nComponents:\n - input (input) params={\"shape\":[1,180]}\n - fc1 (linear) params={\"inFeatures\":180,\"outFeatures\":3610}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3610,\"outFeatures\":3610}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3610,\"outFeatures\":3610}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3610,\"outFeatures\":3610}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3610,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":180,"outFeatures":131}},{"type":"update_params","name":"fc2","params":{"inFeatures":131,"outFeatures":131}},{"type":"update_params","name":"fc3","params":{"inFeatures":131,"outFeatures":131}},{"type":"update_params","name":"fc4","params":{"inFeatures":131,"outFeatures":131}},{"type":"update_params","name":"head","params":{"inFeatures":131,"outFeatures":21}}],"grade":{"pass":true,"score":88,"params":77814}} {"id":"gen-norm-1897","family":"norm","seed":20260716,"spec":"This 92-feature, 71-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1897\nComponents:\n - input (input) params={\"shape\":[1,92]}\n - fc1 (linear) params={\"inFeatures\":92,\"outFeatures\":349}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":349,\"outFeatures\":282}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":282,\"outFeatures\":261}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":261,\"outFeatures\":71}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":349}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":282}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":261}}],"grade":{"pass":true,"score":100,"params":222659}} {"id":"gen-tower-1898","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 30-feature user input and a 22-feature item input, each through its own 2-layer MLP tower ending at width 233, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1898\nComponents:\n - user_input (input) params={\"shape\":[1,30]}\n - item_input (input) params={\"shape\":[1,22]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,30]}},{"componentType":"input","name":"item_input","params":{"shape":[1,22]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":30,"outFeatures":233}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":233,"outFeatures":233}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":22,"outFeatures":233}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":233,"outFeatures":233}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":466,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":121160}} {"id":"gen-grow-1899","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (114-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1899\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":665}},{"type":"update_params","name":"fc2","params":{"inFeatures":665,"outFeatures":665}},{"type":"update_params","name":"head","params":{"inFeatures":665,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":532000}} {"id":"gen-mlp-1900","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 204-feature input with 67 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1900\nComponents:\n - input (input) params={\"shape\":[1,204]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,204]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":204,"outFeatures":120}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":120,"outFeatures":585}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":585,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":133875}} {"id":"gen-ae-1901","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1069-dim input: encode down to a 253-dim bottleneck and decode back to 1069. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1901\nComponents:\n - input (input) params={\"shape\":[1,1069]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1069]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1069,"outFeatures":191}},{"componentType":"linear","name":"enc2","params":{"inFeatures":191,"outFeatures":253}},{"componentType":"linear","name":"dec1","params":{"inFeatures":253,"outFeatures":191}},{"componentType":"linear","name":"dec2","params":{"inFeatures":191,"outFeatures":1069}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":505004}} {"id":"gen-cnn-1902","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 53 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1902\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":28,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":24164}} {"id":"gen-txf-1903","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 331-token sequences with 34 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1903\nComponents:\n - input (input) params={\"shape\":[1,331]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,331]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45503,"embeddingDim":304}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":304,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":14212912}} {"id":"gen-gqa-1904","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 424-token sequences with 12 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1904\nComponents:\n - input (input) params={\"shape\":[1,424]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,424]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33557,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":10204976}} {"id":"gen-trim-1906","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2309-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1906\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":2309}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2309,\"outFeatures\":2309}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2309,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":266}},{"type":"update_params","name":"fc2","params":{"inFeatures":266,"outFeatures":266}},{"type":"update_params","name":"head","params":{"inFeatures":266,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":95494}} {"id":"gen-norm-1907","family":"norm","seed":20260716,"spec":"This 95-feature, 19-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1907\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":265}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":265,\"outFeatures\":136}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":136,\"outFeatures\":41}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":41,\"outFeatures\":433}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":433,\"outFeatures\":349}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":349,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":265}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":136}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":41}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":433}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":349}}],"grade":{"pass":true,"score":100,"params":242292}} {"id":"gen-tower-1908","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 53-feature user input and a 51-feature item input, each through its own 2-layer MLP tower ending at width 72, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1908\nComponents:\n - user_input (input) params={\"shape\":[1,53]}\n - item_input (input) params={\"shape\":[1,51]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,53]}},{"componentType":"input","name":"item_input","params":{"shape":[1,51]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":53,"outFeatures":72}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":72,"outFeatures":72}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":51,"outFeatures":72}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":72,"outFeatures":72}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":18000}} {"id":"gen-grow-1909","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (113-feature input, 6 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1909\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - fc1 (linear) params={\"inFeatures\":113,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":113,"outFeatures":1261}},{"type":"update_params","name":"fc2","params":{"inFeatures":1261,"outFeatures":1261}},{"type":"update_params","name":"head","params":{"inFeatures":1261,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":1740180}} {"id":"gen-mlp-1910","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 22-feature input with 69 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1910\nComponents:\n - input (input) params={\"shape\":[1,22]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,22]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":22,"outFeatures":400}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":400,"outFeatures":993}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":993,"outFeatures":105}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":105,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":517510}} {"id":"gen-ae-1911","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1207-dim input: encode down to a 156-dim bottleneck and decode back to 1207. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1911\nComponents:\n - input (input) params={\"shape\":[1,1207]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1207]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1207,"outFeatures":794}},{"componentType":"linear","name":"enc2","params":{"inFeatures":794,"outFeatures":156}},{"componentType":"linear","name":"dec1","params":{"inFeatures":156,"outFeatures":794}},{"componentType":"linear","name":"dec2","params":{"inFeatures":794,"outFeatures":1207}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2164444}} {"id":"gen-cnn-1912","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 7 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1912\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":48,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":27,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36856}} {"id":"gen-txf-1913","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 452-token sequences with 81 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1913\nComponents:\n - input (input) params={\"shape\":[1,452]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,452]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30597,"embeddingDim":160}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":160,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":160,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5113280}} {"id":"gen-gqa-1914","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 323-token sequences with 12 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1914\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,323]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13053,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5016960}} {"id":"gen-trim-1916","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 4090-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1916\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":4090}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4090,\"outFeatures\":4090}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4090,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":168}},{"type":"update_params","name":"fc2","params":{"inFeatures":168,"outFeatures":168}},{"type":"update_params","name":"head","params":{"inFeatures":168,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":47712}} {"id":"gen-norm-1917","family":"norm","seed":20260716,"spec":"This 147-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1917\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - fc1 (linear) params={\"inFeatures\":147,\"outFeatures\":240}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":240,\"outFeatures\":497}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":497,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":240}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":497}}],"grade":{"pass":true,"score":94,"params":180901}} {"id":"gen-tower-1918","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 110-feature user input and a 142-feature item input, each through its own 2-layer MLP tower ending at width 120, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1918\nComponents:\n - user_input (input) params={\"shape\":[1,110]}\n - item_input (input) params={\"shape\":[1,142]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,110]}},{"componentType":"input","name":"item_input","params":{"shape":[1,142]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":110,"outFeatures":120}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":120,"outFeatures":120}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":142,"outFeatures":120}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":120,"outFeatures":120}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":59280}} {"id":"gen-grow-1919","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (79-feature input, 32 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1919\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - fc1 (linear) params={\"inFeatures\":79,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":79,"outFeatures":863}},{"type":"update_params","name":"fc2","params":{"inFeatures":863,"outFeatures":863}},{"type":"update_params","name":"head","params":{"inFeatures":863,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":840562}} {"id":"gen-mlp-1920","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 223-feature input with 131 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1920\nComponents:\n - input (input) params={\"shape\":[1,223]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,223]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":223,"outFeatures":993}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":993,"outFeatures":820}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":820,"outFeatures":26}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":26,"outFeatures":332}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":332,"outFeatures":433}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":433,"outFeatures":415}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":415,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1443467}} {"id":"gen-ae-1921","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1404-dim input: encode down to a 176-dim bottleneck and decode back to 1404. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1921\nComponents:\n - input (input) params={\"shape\":[1,1404]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1404]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1404,"outFeatures":831}},{"componentType":"linear","name":"enc2","params":{"inFeatures":831,"outFeatures":176}},{"componentType":"linear","name":"dec1","params":{"inFeatures":176,"outFeatures":831}},{"componentType":"linear","name":"dec2","params":{"inFeatures":831,"outFeatures":1404}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2625960}} {"id":"gen-cnn-1922","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 66 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1922\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":25,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":16,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":15,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":21906}} {"id":"gen-txf-1923","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 438-token sequences with 56 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1923\nComponents:\n - input (input) params={\"shape\":[1,438]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,438]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23701,"embeddingDim":384}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":384,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":9712512}} {"id":"gen-gqa-1924","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 215-token sequences with 86 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1924\nComponents:\n - input (input) params={\"shape\":[1,215]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,215]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45938,"embeddingDim":432}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":432,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[432]}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":19882368}} {"id":"gen-trim-1926","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3491-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1926\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - fc1 (linear) params={\"inFeatures\":138,\"outFeatures\":3491}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3491,\"outFeatures\":3491}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3491,\"outFeatures\":3491}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3491,\"outFeatures\":3491}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3491,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":138,"outFeatures":256}},{"type":"update_params","name":"fc2","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"fc3","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"fc4","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"head","params":{"inFeatures":256,"outFeatures":22}}],"grade":{"pass":true,"score":88,"params":237568}} {"id":"gen-norm-1927","family":"norm","seed":20260716,"spec":"This 67-feature, 8-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1927\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":273}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":273,\"outFeatures\":162}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":162,\"outFeatures\":423}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":423,\"outFeatures\":473}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":473,\"outFeatures\":338}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":338,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":273}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":162}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":423}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":473}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":338}}],"grade":{"pass":true,"score":100,"params":493700}} {"id":"gen-tower-1928","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 27-feature user input and a 200-feature item input, each through its own 2-layer MLP tower ending at width 39, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1928\nComponents:\n - user_input (input) params={\"shape\":[1,27]}\n - item_input (input) params={\"shape\":[1,200]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,27]}},{"componentType":"input","name":"item_input","params":{"shape":[1,200]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":27,"outFeatures":39}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":39,"outFeatures":39}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":200,"outFeatures":39}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":39,"outFeatures":39}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":11973}} {"id":"gen-grow-1929","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (118-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1929\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":118,"outFeatures":1093}},{"type":"update_params","name":"fc2","params":{"inFeatures":1093,"outFeatures":1093}},{"type":"update_params","name":"head","params":{"inFeatures":1093,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":1359692}} {"id":"gen-mlp-1930","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 63-feature input with 117 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1930\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,63]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":63,"outFeatures":206}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":206,"outFeatures":41}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":41,"outFeatures":117}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":26221}} {"id":"gen-ae-1931","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1861-dim input: encode down to a 92-dim bottleneck and decode back to 1861. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1931\nComponents:\n - input (input) params={\"shape\":[1,1861]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1861]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1861,"outFeatures":905}},{"componentType":"linear","name":"enc2","params":{"inFeatures":905,"outFeatures":92}},{"componentType":"linear","name":"dec1","params":{"inFeatures":92,"outFeatures":905}},{"componentType":"linear","name":"dec2","params":{"inFeatures":905,"outFeatures":1861}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3534930}} {"id":"gen-cnn-1932","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 41 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1932\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":3380}} {"id":"gen-txf-1933","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 393-token sequences with 6 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1933\nComponents:\n - input (input) params={\"shape\":[1,393]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,393]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37552,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2953860}} {"id":"gen-gqa-1934","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 147-token sequences with 60 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1934\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,147]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28251,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":11324400}} {"id":"gen-trim-1936","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1890-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1936\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":1890}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1890,\"outFeatures\":1890}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1890,\"outFeatures\":1890}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1890,\"outFeatures\":1890}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1890,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":354}},{"type":"update_params","name":"fc2","params":{"inFeatures":354,"outFeatures":354}},{"type":"update_params","name":"fc3","params":{"inFeatures":354,"outFeatures":354}},{"type":"update_params","name":"fc4","params":{"inFeatures":354,"outFeatures":354}},{"type":"update_params","name":"head","params":{"inFeatures":354,"outFeatures":45}}],"grade":{"pass":true,"score":88,"params":414888}} {"id":"gen-norm-1937","family":"norm","seed":20260716,"spec":"This 134-feature, 22-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1937\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - fc1 (linear) params={\"inFeatures\":134,\"outFeatures\":62}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":62,\"outFeatures\":509}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":509,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":62}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":509}}],"grade":{"pass":true,"score":94,"params":51064}} {"id":"gen-tower-1938","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 71-feature user input and a 53-feature item input, each through its own 2-layer MLP tower ending at width 369, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1938\nComponents:\n - user_input (input) params={\"shape\":[1,71]}\n - item_input (input) params={\"shape\":[1,53]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,71]}},{"componentType":"input","name":"item_input","params":{"shape":[1,53]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":71,"outFeatures":369}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":369,"outFeatures":369}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":53,"outFeatures":369}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":369,"outFeatures":369}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":738,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":318816}} {"id":"gen-grow-1939","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (57-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1939\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":57,"outFeatures":948}},{"type":"update_params","name":"fc2","params":{"inFeatures":948,"outFeatures":948}},{"type":"update_params","name":"head","params":{"inFeatures":948,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":955584}} {"id":"gen-mlp-1940","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 386-feature input with 192 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1940\nComponents:\n - input (input) params={\"shape\":[1,386]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,386]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":386,"outFeatures":730}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":730,"outFeatures":690}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":690,"outFeatures":903}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":903,"outFeatures":51}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":192}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1464395}} {"id":"gen-ae-1941","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1581-dim input: encode down to a 163-dim bottleneck and decode back to 1581. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1941\nComponents:\n - input (input) params={\"shape\":[1,1581]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1581]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1581,"outFeatures":836}},{"componentType":"linear","name":"enc2","params":{"inFeatures":836,"outFeatures":163}},{"componentType":"linear","name":"dec1","params":{"inFeatures":163,"outFeatures":836}},{"componentType":"linear","name":"dec2","params":{"inFeatures":836,"outFeatures":1581}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2915968}} {"id":"gen-cnn-1942","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 52 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1942\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":55,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":35,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":49060}} {"id":"gen-txf-1943","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 357-token sequences with 4 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1943\nComponents:\n - input (input) params={\"shape\":[1,357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,357]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25552,"embeddingDim":128}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":128,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3336704}} {"id":"gen-gqa-1944","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 363-token sequences with 68 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1944\nComponents:\n - input (input) params={\"shape\":[1,363]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,363]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37811,"embeddingDim":164}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[164]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[164]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[164]}},{"componentType":"linear","name":"head","params":{"inFeatures":164,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6212156}} {"id":"gen-trim-1946","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2365-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1946\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - fc1 (linear) params={\"inFeatures\":43,\"outFeatures\":2365}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2365,\"outFeatures\":2365}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2365,\"outFeatures\":2365}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2365,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":43,"outFeatures":220}},{"type":"update_params","name":"fc2","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"fc3","params":{"inFeatures":220,"outFeatures":220}},{"type":"update_params","name":"head","params":{"inFeatures":220,"outFeatures":48}}],"grade":{"pass":true,"score":84,"params":116820}} {"id":"gen-norm-1947","family":"norm","seed":20260716,"spec":"This 216-feature, 72-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1947\nComponents:\n - input (input) params={\"shape\":[1,216]}\n - fc1 (linear) params={\"inFeatures\":216,\"outFeatures\":415}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":415,\"outFeatures\":507}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":507,\"outFeatures\":269}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":269,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":415}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":507}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":269}}],"grade":{"pass":true,"score":100,"params":455796}} {"id":"gen-tower-1948","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 149-feature user input and a 188-feature item input, each through its own 2-layer MLP tower ending at width 47, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1948\nComponents:\n - user_input (input) params={\"shape\":[1,149]}\n - item_input (input) params={\"shape\":[1,188]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,149]}},{"componentType":"input","name":"item_input","params":{"shape":[1,188]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":149,"outFeatures":47}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":47,"outFeatures":47}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":188,"outFeatures":47}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":47,"outFeatures":47}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":94,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":20351}} {"id":"gen-grow-1949","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (78-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1949\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - fc1 (linear) params={\"inFeatures\":78,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":78,"outFeatures":1068}},{"type":"update_params","name":"fc2","params":{"inFeatures":1068,"outFeatures":1068}},{"type":"update_params","name":"head","params":{"inFeatures":1068,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":1243152}} {"id":"gen-mlp-1950","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 38-feature input with 12 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1950\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,38]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":38,"outFeatures":821}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":821,"outFeatures":925}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":925,"outFeatures":1000}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1000,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1727623}} {"id":"gen-ae-1951","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 870-dim input: encode down to a 184-dim bottleneck and decode back to 870. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1951\nComponents:\n - input (input) params={\"shape\":[1,870]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,870]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":870,"outFeatures":630}},{"componentType":"linear","name":"enc2","params":{"inFeatures":630,"outFeatures":184}},{"componentType":"linear","name":"dec1","params":{"inFeatures":184,"outFeatures":630}},{"componentType":"linear","name":"dec2","params":{"inFeatures":630,"outFeatures":870}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1328040}} {"id":"gen-cnn-1952","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x30x30 image with 16 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1952\nComponents:\n - input (input) params={\"shape\":[1,3,30,30]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,30,30]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":30,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":53,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":27,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":43659}} {"id":"gen-txf-1953","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 152-token sequences with 74 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1953\nComponents:\n - input (input) params={\"shape\":[1,152]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,152]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25763,"embeddingDim":96}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":96,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":96,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2554080}} {"id":"gen-gqa-1954","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 154-token sequences with 77 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1954\nComponents:\n - input (input) params={\"shape\":[1,154]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,154]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27078,"embeddingDim":200}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":200,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[200]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":200,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[200]}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5431000}} {"id":"gen-trim-1956","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2769-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1956\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - fc1 (linear) params={\"inFeatures\":93,\"outFeatures\":2769}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2769,\"outFeatures\":2769}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2769,\"outFeatures\":2769}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2769,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":93,"outFeatures":289}},{"type":"update_params","name":"fc2","params":{"inFeatures":289,"outFeatures":289}},{"type":"update_params","name":"fc3","params":{"inFeatures":289,"outFeatures":289}},{"type":"update_params","name":"head","params":{"inFeatures":289,"outFeatures":30}}],"grade":{"pass":true,"score":84,"params":202589}} {"id":"gen-norm-1957","family":"norm","seed":20260716,"spec":"This 178-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1957\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - fc1 (linear) params={\"inFeatures\":178,\"outFeatures\":211}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":211,\"outFeatures\":207}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":207,\"outFeatures\":377}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":377,\"outFeatures\":308}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":308,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":211}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":207}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":377}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":308}}],"grade":{"pass":true,"score":100,"params":293870}} {"id":"gen-tower-1958","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 62-feature user input and a 35-feature item input, each through its own 2-layer MLP tower ending at width 493, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1958\nComponents:\n - user_input (input) params={\"shape\":[1,62]}\n - item_input (input) params={\"shape\":[1,35]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,62]}},{"componentType":"input","name":"item_input","params":{"shape":[1,35]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":62,"outFeatures":493}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":493,"outFeatures":493}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":35,"outFeatures":493}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":493,"outFeatures":493}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":986,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":534905}} {"id":"gen-grow-1959","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (80-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1959\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":80,"outFeatures":1049}},{"type":"update_params","name":"fc2","params":{"inFeatures":1049,"outFeatures":1049}},{"type":"update_params","name":"head","params":{"inFeatures":1049,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":1200056}} {"id":"gen-mlp-1960","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 449-feature input with 12 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1960\nComponents:\n - input (input) params={\"shape\":[1,449]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,449]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":449,"outFeatures":147}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":147,"outFeatures":769}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":769,"outFeatures":464}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":464,"outFeatures":348}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":348,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":701510}} {"id":"gen-ae-1961","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1730-dim input: encode down to a 206-dim bottleneck and decode back to 1730. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1961\nComponents:\n - input (input) params={\"shape\":[1,1730]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1730]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1730,"outFeatures":1005}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1005,"outFeatures":206}},{"componentType":"linear","name":"dec1","params":{"inFeatures":206,"outFeatures":1005}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1005,"outFeatures":1730}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3891360}} {"id":"gen-cnn-1962","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 80 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1962\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":11,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":22,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":55,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":26015}} {"id":"gen-txf-1963","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 49-token sequences with 35 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1963\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,49]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44647,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":120,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":120,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5534640}} {"id":"gen-gqa-1964","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 157-token sequences with 86 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1964\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,157]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16673,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[400]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6703600}} {"id":"gen-trim-1966","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3209-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1966\nComponents:\n - input (input) params={\"shape\":[1,248]}\n - fc1 (linear) params={\"inFeatures\":248,\"outFeatures\":3209}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3209,\"outFeatures\":3209}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3209,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":248,"outFeatures":242}},{"type":"update_params","name":"fc2","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"head","params":{"inFeatures":242,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":121484}} {"id":"gen-norm-1967","family":"norm","seed":20260716,"spec":"This 57-feature, 34-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1967\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":181}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":181,\"outFeatures\":106}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":106,\"outFeatures\":466}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":466,\"outFeatures\":491}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":491,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":181}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":106}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":466}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":491}}],"grade":{"pass":true,"score":100,"params":324399}} {"id":"gen-tower-1968","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 200-feature user input and a 104-feature item input, each through its own 2-layer MLP tower ending at width 223, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1968\nComponents:\n - user_input (input) params={\"shape\":[1,200]}\n - item_input (input) params={\"shape\":[1,104]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,200]}},{"componentType":"input","name":"item_input","params":{"shape":[1,104]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":200,"outFeatures":223}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":223,"outFeatures":223}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":104,"outFeatures":223}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":223,"outFeatures":223}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":446,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":167696}} {"id":"gen-grow-1969","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (125-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1969\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - fc1 (linear) params={\"inFeatures\":125,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":125,"outFeatures":895}},{"type":"update_params","name":"fc2","params":{"inFeatures":895,"outFeatures":895}},{"type":"update_params","name":"head","params":{"inFeatures":895,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":934380}} {"id":"gen-mlp-1970","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 480-feature input with 19 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1970\nComponents:\n - input (input) params={\"shape\":[1,480]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,480]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":480,"outFeatures":505}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":505,"outFeatures":931}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":931,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":730244}} {"id":"gen-ae-1971","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 337-dim input: encode down to a 160-dim bottleneck and decode back to 337. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1971\nComponents:\n - input (input) params={\"shape\":[1,337]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,337]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":337,"outFeatures":879}},{"componentType":"linear","name":"enc2","params":{"inFeatures":879,"outFeatures":160}},{"componentType":"linear","name":"dec1","params":{"inFeatures":160,"outFeatures":879}},{"componentType":"linear","name":"dec2","params":{"inFeatures":879,"outFeatures":337}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":873726}} {"id":"gen-cnn-1972","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x57x57 image with 59 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1972\nComponents:\n - input (input) params={\"shape\":[1,3,57,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,57,57]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":26690}} {"id":"gen-txf-1973","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 374-token sequences with 95 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1973\nComponents:\n - input (input) params={\"shape\":[1,374]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,374]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23468,"embeddingDim":84}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":84,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2007516}} {"id":"gen-gqa-1974","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 150-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1974\nComponents:\n - input (input) params={\"shape\":[1,150]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,150]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42329,"embeddingDim":148}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":148,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[148]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":148,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[148]}},{"componentType":"linear","name":"head","params":{"inFeatures":148,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6275792}} {"id":"gen-trim-1976","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3855-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1976\nComponents:\n - input (input) params={\"shape\":[1,169]}\n - fc1 (linear) params={\"inFeatures\":169,\"outFeatures\":3855}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3855,\"outFeatures\":3855}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3855,\"outFeatures\":3855}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3855,\"outFeatures\":3855}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3855,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":169,"outFeatures":223}},{"type":"update_params","name":"fc2","params":{"inFeatures":223,"outFeatures":223}},{"type":"update_params","name":"fc3","params":{"inFeatures":223,"outFeatures":223}},{"type":"update_params","name":"fc4","params":{"inFeatures":223,"outFeatures":223}},{"type":"update_params","name":"head","params":{"inFeatures":223,"outFeatures":18}}],"grade":{"pass":true,"score":88,"params":190888}} {"id":"gen-norm-1977","family":"norm","seed":20260716,"spec":"This 110-feature, 61-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1977\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":384}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":384,\"outFeatures\":203}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":203,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":384}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":203}}],"grade":{"pass":true,"score":94,"params":132575}} {"id":"gen-tower-1978","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 149-feature user input and a 138-feature item input, each through its own 2-layer MLP tower ending at width 425, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1978\nComponents:\n - user_input (input) params={\"shape\":[1,149]}\n - item_input (input) params={\"shape\":[1,138]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,149]}},{"componentType":"input","name":"item_input","params":{"shape":[1,138]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":149,"outFeatures":425}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":138,"outFeatures":425}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":850,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":484075}} {"id":"gen-grow-1979","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1979\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":963}},{"type":"update_params","name":"fc2","params":{"inFeatures":963,"outFeatures":963}},{"type":"update_params","name":"head","params":{"inFeatures":963,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":989001}} {"id":"gen-mlp-1980","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 305-feature input with 72 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1980\nComponents:\n - input (input) params={\"shape\":[1,305]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,305]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":305,"outFeatures":319}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":319,"outFeatures":969}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":969,"outFeatures":328}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":328,"outFeatures":809}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":809,"outFeatures":897}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":897,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1779847}} {"id":"gen-ae-1981","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1487-dim input: encode down to a 148-dim bottleneck and decode back to 1487. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1981\nComponents:\n - input (input) params={\"shape\":[1,1487]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1487]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1487,"outFeatures":652}},{"componentType":"linear","name":"enc2","params":{"inFeatures":652,"outFeatures":148}},{"componentType":"linear","name":"dec1","params":{"inFeatures":148,"outFeatures":652}},{"componentType":"linear","name":"dec2","params":{"inFeatures":652,"outFeatures":1487}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2132040}} {"id":"gen-cnn-1982","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 95 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1982\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":43,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":14880}} {"id":"gen-txf-1983","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 147-token sequences with 40 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1983\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,147]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18698,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":104,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2078544}} {"id":"gen-gqa-1984","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 151-token sequences with 46 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1984\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,151]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16055,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1674504}} {"id":"gen-trim-1986","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2039-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1986\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":2039}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2039,\"outFeatures\":2039}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2039,\"outFeatures\":2039}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2039,\"outFeatures\":2039}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2039,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":145,"outFeatures":126}},{"type":"update_params","name":"fc2","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"fc3","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"fc4","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"head","params":{"inFeatures":126,"outFeatures":44}}],"grade":{"pass":true,"score":88,"params":71442}} {"id":"gen-norm-1987","family":"norm","seed":20260716,"spec":"This 51-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1987\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":32}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":32,\"outFeatures\":460}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":460,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":32}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":460}}],"grade":{"pass":true,"score":94,"params":35672}} {"id":"gen-tower-1988","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 23-feature user input and a 246-feature item input, each through its own 2-layer MLP tower ending at width 432, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1988\nComponents:\n - user_input (input) params={\"shape\":[1,23]}\n - item_input (input) params={\"shape\":[1,246]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,23]}},{"componentType":"input","name":"item_input","params":{"shape":[1,246]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":23,"outFeatures":432}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":432,"outFeatures":432}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":246,"outFeatures":432}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":432,"outFeatures":432}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":864,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":490320}} {"id":"gen-grow-1989","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (104-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1989\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":1232}},{"type":"update_params","name":"fc2","params":{"inFeatures":1232,"outFeatures":1232}},{"type":"update_params","name":"head","params":{"inFeatures":1232,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":1689072}} {"id":"gen-mlp-1990","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 403-feature input with 11 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-1990\nComponents:\n - input (input) params={\"shape\":[1,403]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,403]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":403,"outFeatures":455}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":455,"outFeatures":622}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":622,"outFeatures":316}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":316,"outFeatures":420}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":420,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":800267}} {"id":"gen-ae-1991","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1357-dim input: encode down to a 123-dim bottleneck and decode back to 1357. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-1991\nComponents:\n - input (input) params={\"shape\":[1,1357]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1357]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1357,"outFeatures":488}},{"componentType":"linear","name":"enc2","params":{"inFeatures":488,"outFeatures":123}},{"componentType":"linear","name":"dec1","params":{"inFeatures":123,"outFeatures":488}},{"componentType":"linear","name":"dec2","params":{"inFeatures":488,"outFeatures":1357}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1444480}} {"id":"gen-cnn-1992","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 42 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-1992\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":27,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":17,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":12,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":15096}} {"id":"gen-txf-1993","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 259-token sequences with 8 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-1993\nComponents:\n - input (input) params={\"shape\":[1,259]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,259]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18166,"embeddingDim":44}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":44,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":807400}} {"id":"gen-gqa-1994","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 491-token sequences with 32 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-1994\nComponents:\n - input (input) params={\"shape\":[1,491]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,491]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36191,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":624,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":22603152}} {"id":"gen-fix-1995","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (324) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-1995\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - embed (embedding) params={\"numEmbeddings\":9566,\"embeddingDim\":324}\n - attn (multiHeadAttention) params={\"embedDim\":324,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":324,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":3542940}} {"id":"gen-trim-1996","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2595-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-1996\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":2595}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2595,\"outFeatures\":2595}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2595,\"outFeatures\":2595}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2595,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":271}},{"type":"update_params","name":"fc2","params":{"inFeatures":271,"outFeatures":271}},{"type":"update_params","name":"fc3","params":{"inFeatures":271,"outFeatures":271}},{"type":"update_params","name":"head","params":{"inFeatures":271,"outFeatures":22}}],"grade":{"pass":true,"score":84,"params":163142}} {"id":"gen-norm-1997","family":"norm","seed":20260716,"spec":"This 201-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-1997\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - fc1 (linear) params={\"inFeatures\":201,\"outFeatures\":329}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":329,\"outFeatures\":467}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":467,\"outFeatures\":126}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":126,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":329}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":467}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":126}}],"grade":{"pass":true,"score":100,"params":283402}} {"id":"gen-tower-1998","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 43-feature user input and a 208-feature item input, each through its own 2-layer MLP tower ending at width 47, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-1998\nComponents:\n - user_input (input) params={\"shape\":[1,43]}\n - item_input (input) params={\"shape\":[1,208]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,43]}},{"componentType":"input","name":"item_input","params":{"shape":[1,208]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":43,"outFeatures":47}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":47,"outFeatures":47}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":208,"outFeatures":47}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":47,"outFeatures":47}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":94,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":16309}} {"id":"gen-grow-1999","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (37-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-1999\nComponents:\n - input (input) params={\"shape\":[1,37]}\n - fc1 (linear) params={\"inFeatures\":37,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":37,"outFeatures":741}},{"type":"update_params","name":"fc2","params":{"inFeatures":741,"outFeatures":741}},{"type":"update_params","name":"head","params":{"inFeatures":741,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":602433}} {"id":"gen-mlp-2000","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 170-feature input with 114 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2000\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,170]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":170,"outFeatures":336}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":336,"outFeatures":782}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":782,"outFeatures":196}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":196,"outFeatures":568}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":568,"outFeatures":204}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":204,"outFeatures":114}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":723600}} {"id":"gen-ae-2001","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2048-dim input: encode down to a 98-dim bottleneck and decode back to 2048. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2001\nComponents:\n - input (input) params={\"shape\":[1,2048]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2048]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2048,"outFeatures":760}},{"componentType":"linear","name":"enc2","params":{"inFeatures":760,"outFeatures":98}},{"componentType":"linear","name":"dec1","params":{"inFeatures":98,"outFeatures":760}},{"componentType":"linear","name":"dec2","params":{"inFeatures":760,"outFeatures":2048}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3261920}} {"id":"gen-cnn-2002","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 40 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2002\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":47,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":45,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":55360}} {"id":"gen-txf-2003","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 334-token sequences with 58 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2003\nComponents:\n - input (input) params={\"shape\":[1,334]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,334]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47241,"embeddingDim":28}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":28,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":28,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":28,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1333780}} {"id":"gen-gqa-2004","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 160-token sequences with 100 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2004\nComponents:\n - input (input) params={\"shape\":[1,160]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,160]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45634,"embeddingDim":60}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":60,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[60]}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2744040}} {"id":"gen-trim-2006","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2536-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2006\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - fc1 (linear) params={\"inFeatures\":124,\"outFeatures\":2536}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2536,\"outFeatures\":2536}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2536,\"outFeatures\":2536}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2536,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":124,"outFeatures":286}},{"type":"update_params","name":"fc2","params":{"inFeatures":286,"outFeatures":286}},{"type":"update_params","name":"fc3","params":{"inFeatures":286,"outFeatures":286}},{"type":"update_params","name":"head","params":{"inFeatures":286,"outFeatures":29}}],"grade":{"pass":true,"score":84,"params":207350}} {"id":"gen-norm-2007","family":"norm","seed":20260716,"spec":"This 20-feature, 16-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2007\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":462}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":462,\"outFeatures\":272}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":272,\"outFeatures\":433}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":433,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":462}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":272}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":433}}],"grade":{"pass":true,"score":100,"params":259608}} {"id":"gen-tower-2008","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 241-feature user input and a 253-feature item input, each through its own 2-layer MLP tower ending at width 304, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2008\nComponents:\n - user_input (input) params={\"shape\":[1,241]}\n - item_input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,241]}},{"componentType":"input","name":"item_input","params":{"shape":[1,253]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":241,"outFeatures":304}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":304,"outFeatures":304}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":253,"outFeatures":304}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":304,"outFeatures":304}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":608,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":335616}} {"id":"gen-grow-2009","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (115-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2009\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":1384}},{"type":"update_params","name":"fc2","params":{"inFeatures":1384,"outFeatures":1384}},{"type":"update_params","name":"head","params":{"inFeatures":1384,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":2123056}} {"id":"gen-mlp-2010","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 318-feature input with 147 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2010\nComponents:\n - input (input) params={\"shape\":[1,318]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,318]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":318,"outFeatures":91}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":91,"outFeatures":957}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":957,"outFeatures":147}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":256704}} {"id":"gen-ae-2011","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1305-dim input: encode down to a 115-dim bottleneck and decode back to 1305. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2011\nComponents:\n - input (input) params={\"shape\":[1,1305]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1305]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1305,"outFeatures":260}},{"componentType":"linear","name":"enc2","params":{"inFeatures":260,"outFeatures":115}},{"componentType":"linear","name":"dec1","params":{"inFeatures":115,"outFeatures":260}},{"componentType":"linear","name":"dec2","params":{"inFeatures":260,"outFeatures":1305}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":738400}} {"id":"gen-cnn-2012","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 67 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2012\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":34,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":19,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37600}} {"id":"gen-txf-2013","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 278-token sequences with 80 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2013\nComponents:\n - input (input) params={\"shape\":[1,278]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,278]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26350,"embeddingDim":368}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":368,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":10267936}} {"id":"gen-gqa-2014","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 373-token sequences with 70 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2014\nComponents:\n - input (input) params={\"shape\":[1,373]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,373]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41548,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":136,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5660048}} {"id":"gen-fix-2015","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (462) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2015\nComponents:\n - input (input) params={\"shape\":[1,48]}\n - embed (embedding) params={\"numEmbeddings\":32723,\"embeddingDim\":462}\n - attn (multiHeadAttention) params={\"embedDim\":462,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":462,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":16011996}} {"id":"gen-trim-2016","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3102-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2016\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":3102}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3102,\"outFeatures\":3102}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3102,\"outFeatures\":3102}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3102,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":277}},{"type":"update_params","name":"fc2","params":{"inFeatures":277,"outFeatures":277}},{"type":"update_params","name":"fc3","params":{"inFeatures":277,"outFeatures":277}},{"type":"update_params","name":"head","params":{"inFeatures":277,"outFeatures":34}}],"grade":{"pass":true,"score":84,"params":195285}} {"id":"gen-norm-2017","family":"norm","seed":20260716,"spec":"This 168-feature, 48-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2017\nComponents:\n - input (input) params={\"shape\":[1,168]}\n - fc1 (linear) params={\"inFeatures\":168,\"outFeatures\":394}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":394,\"outFeatures\":40}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":40,\"outFeatures\":82}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":82,\"outFeatures\":299}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":299,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":394}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":40}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":82}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":299}}],"grade":{"pass":true,"score":100,"params":124102}} {"id":"gen-tower-2018","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 151-feature user input and a 197-feature item input, each through its own 2-layer MLP tower ending at width 197, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2018\nComponents:\n - user_input (input) params={\"shape\":[1,151]}\n - item_input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,151]}},{"componentType":"input","name":"item_input","params":{"shape":[1,197]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":151,"outFeatures":197}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":197,"outFeatures":197}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":197,"outFeatures":197}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":197,"outFeatures":197}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":394,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":146568}} {"id":"gen-grow-2019","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (118-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2019\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":118,"outFeatures":1341}},{"type":"update_params","name":"fc2","params":{"inFeatures":1341,"outFeatures":1341}},{"type":"update_params","name":"head","params":{"inFeatures":1341,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1992726}} {"id":"gen-mlp-2020","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 165-feature input with 119 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2020\nComponents:\n - input (input) params={\"shape\":[1,165]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,165]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":165,"outFeatures":981}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":981,"outFeatures":510}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":510,"outFeatures":338}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":338,"outFeatures":646}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":646,"outFeatures":432}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":119}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1383383}} {"id":"gen-ae-2021","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1765-dim input: encode down to a 166-dim bottleneck and decode back to 1765. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2021\nComponents:\n - input (input) params={\"shape\":[1,1765]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1765]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1765,"outFeatures":146}},{"componentType":"linear","name":"enc2","params":{"inFeatures":146,"outFeatures":166}},{"componentType":"linear","name":"dec1","params":{"inFeatures":166,"outFeatures":146}},{"componentType":"linear","name":"dec2","params":{"inFeatures":146,"outFeatures":1765}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":563852}} {"id":"gen-cnn-2022","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 65 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2022\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":42,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":7336}} {"id":"gen-txf-2023","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 132-token sequences with 59 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2023\nComponents:\n - input (input) params={\"shape\":[1,132]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,132]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27356,"embeddingDim":400}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":400,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":400,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":12246000}} {"id":"gen-gqa-2024","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 46-token sequences with 91 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2024\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,46]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13996,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4733232}} {"id":"gen-trim-2026","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2844-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2026\nComponents:\n - input (input) params={\"shape\":[1,235]}\n - fc1 (linear) params={\"inFeatures\":235,\"outFeatures\":2844}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2844,\"outFeatures\":2844}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2844,\"outFeatures\":2844}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2844,\"outFeatures\":2844}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2844,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":235,"outFeatures":347}},{"type":"update_params","name":"fc2","params":{"inFeatures":347,"outFeatures":347}},{"type":"update_params","name":"fc3","params":{"inFeatures":347,"outFeatures":347}},{"type":"update_params","name":"fc4","params":{"inFeatures":347,"outFeatures":347}},{"type":"update_params","name":"head","params":{"inFeatures":347,"outFeatures":23}}],"grade":{"pass":true,"score":88,"params":450753}} {"id":"gen-norm-2027","family":"norm","seed":20260716,"spec":"This 69-feature, 29-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2027\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - fc1 (linear) params={\"inFeatures\":69,\"outFeatures\":309}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":309,\"outFeatures\":302}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":302,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":309}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":302}}],"grade":{"pass":true,"score":94,"params":123397}} {"id":"gen-tower-2028","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 20-feature user input and a 203-feature item input, each through its own 2-layer MLP tower ending at width 414, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2028\nComponents:\n - user_input (input) params={\"shape\":[1,20]}\n - item_input (input) params={\"shape\":[1,203]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,20]}},{"componentType":"input","name":"item_input","params":{"shape":[1,203]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":20,"outFeatures":414}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":414,"outFeatures":414}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":203,"outFeatures":414}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":414,"outFeatures":414}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":828,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":435942}} {"id":"gen-grow-2029","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (90-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2029\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":90,"outFeatures":898}},{"type":"update_params","name":"fc2","params":{"inFeatures":898,"outFeatures":898}},{"type":"update_params","name":"head","params":{"inFeatures":898,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":908776}} {"id":"gen-mlp-2030","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 348-feature input with 116 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2030\nComponents:\n - input (input) params={\"shape\":[1,348]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,348]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":348,"outFeatures":701}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":701,"outFeatures":511}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":511,"outFeatures":116}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":661435}} {"id":"gen-ae-2031","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 815-dim input: encode down to a 225-dim bottleneck and decode back to 815. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2031\nComponents:\n - input (input) params={\"shape\":[1,815]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,815]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":815,"outFeatures":358}},{"componentType":"linear","name":"enc2","params":{"inFeatures":358,"outFeatures":225}},{"componentType":"linear","name":"dec1","params":{"inFeatures":225,"outFeatures":358}},{"componentType":"linear","name":"dec2","params":{"inFeatures":358,"outFeatures":815}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":744640}} {"id":"gen-cnn-2032","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 98 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2032\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":8,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":16208}} {"id":"gen-txf-2033","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 313-token sequences with 60 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2033\nComponents:\n - input (input) params={\"shape\":[1,313]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,313]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11254,"embeddingDim":196}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":196,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":196,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":196,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":196,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2678536}} {"id":"gen-gqa-2034","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 398-token sequences with 13 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2034\nComponents:\n - input (input) params={\"shape\":[1,398]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,398]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15845,"embeddingDim":112}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":112,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[112]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":112,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[112]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":112,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[112]}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1776096}} {"id":"gen-fix-2035","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (384) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2035\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - embed (embedding) params={\"numEmbeddings\":36189,\"embeddingDim\":384}\n - attn (multiHeadAttention) params={\"embedDim\":384,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":384,\"outFeatures\":89}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":14520576}} {"id":"gen-trim-2036","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2707-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2036\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":2707}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2707,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":145,"outFeatures":263}},{"type":"update_params","name":"fc2","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"fc3","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"fc4","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"head","params":{"inFeatures":263,"outFeatures":7}}],"grade":{"pass":true,"score":88,"params":247483}} {"id":"gen-norm-2037","family":"norm","seed":20260716,"spec":"This 108-feature, 91-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2037\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":95}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":95,\"outFeatures\":167}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":167,\"outFeatures\":392}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":392,\"outFeatures\":91}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":95}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":167}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":392}}],"grade":{"pass":true,"score":100,"params":127261}} {"id":"gen-tower-2038","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 204-feature user input and a 96-feature item input, each through its own 2-layer MLP tower ending at width 32, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2038\nComponents:\n - user_input (input) params={\"shape\":[1,204]}\n - item_input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,204]}},{"componentType":"input","name":"item_input","params":{"shape":[1,96]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":204,"outFeatures":32}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":32,"outFeatures":32}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":96,"outFeatures":32}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":32,"outFeatures":32}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":11712}} {"id":"gen-grow-2039","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (102-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2039\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - fc1 (linear) params={\"inFeatures\":102,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":102,"outFeatures":1395}},{"type":"update_params","name":"fc2","params":{"inFeatures":1395,"outFeatures":1395}},{"type":"update_params","name":"head","params":{"inFeatures":1395,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":2091105}} {"id":"gen-mlp-2040","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 24-feature input with 162 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2040\nComponents:\n - input (input) params={\"shape\":[1,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,24]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":24,"outFeatures":265}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":265,"outFeatures":266}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":266,"outFeatures":961}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":961,"outFeatures":582}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":582,"outFeatures":779}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":779,"outFeatures":966}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":966,"outFeatures":162}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2254162}} {"id":"gen-ae-2041","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 784-dim input: encode down to a 59-dim bottleneck and decode back to 784. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2041\nComponents:\n - input (input) params={\"shape\":[1,784]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,784]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":784,"outFeatures":712}},{"componentType":"linear","name":"enc2","params":{"inFeatures":712,"outFeatures":59}},{"componentType":"linear","name":"dec1","params":{"inFeatures":59,"outFeatures":712}},{"componentType":"linear","name":"dec2","params":{"inFeatures":712,"outFeatures":784}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1200432}} {"id":"gen-cnn-2042","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 4 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2042\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":25,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":32475}} {"id":"gen-txf-2043","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 99-token sequences with 22 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2043\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,99]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20337,"embeddingDim":416}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":416,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":416,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":416,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":10546016}} {"id":"gen-gqa-2044","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 106-token sequences with 93 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2044\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,106]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44851,"embeddingDim":52}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":52,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[52]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":52,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[52]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":52,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[52]}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2337088}} {"id":"gen-fix-2045","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (98) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2045\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - embed (embedding) params={\"numEmbeddings\":45096,\"embeddingDim\":98}\n - attn (multiHeadAttention) params={\"embedDim\":98,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":98,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4458216}} {"id":"gen-trim-2046","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2279-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2046\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - fc1 (linear) params={\"inFeatures\":55,\"outFeatures\":2279}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2279,\"outFeatures\":2279}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2279,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":55,"outFeatures":377}},{"type":"update_params","name":"fc2","params":{"inFeatures":377,"outFeatures":377}},{"type":"update_params","name":"head","params":{"inFeatures":377,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":180960}} {"id":"gen-norm-2047","family":"norm","seed":20260716,"spec":"This 135-feature, 85-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2047\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - fc1 (linear) params={\"inFeatures\":135,\"outFeatures\":323}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":323,\"outFeatures\":461}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":461,\"outFeatures\":310}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":310,\"outFeatures\":415}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":415,\"outFeatures\":85}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":323}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":461}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":310}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":415}}],"grade":{"pass":true,"score":100,"params":499343}} {"id":"gen-tower-2048","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 113-feature user input and a 197-feature item input, each through its own 2-layer MLP tower ending at width 271, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2048\nComponents:\n - user_input (input) params={\"shape\":[1,113]}\n - item_input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,113]}},{"componentType":"input","name":"item_input","params":{"shape":[1,197]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":113,"outFeatures":271}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":197,"outFeatures":271}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":542,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":231434}} {"id":"gen-grow-2049","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2049\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":885}},{"type":"update_params","name":"fc2","params":{"inFeatures":885,"outFeatures":885}},{"type":"update_params","name":"head","params":{"inFeatures":885,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":905355}} {"id":"gen-mlp-2050","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 451-feature input with 103 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2050\nComponents:\n - input (input) params={\"shape\":[1,451]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,451]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":451,"outFeatures":405}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":405,"outFeatures":605}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":605,"outFeatures":286}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":286,"outFeatures":468}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":468,"outFeatures":103}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":782762}} {"id":"gen-ae-2051","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2008-dim input: encode down to a 142-dim bottleneck and decode back to 2008. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2051\nComponents:\n - input (input) params={\"shape\":[1,2008]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2008]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2008,"outFeatures":85}},{"componentType":"linear","name":"enc2","params":{"inFeatures":85,"outFeatures":142}},{"componentType":"linear","name":"dec1","params":{"inFeatures":142,"outFeatures":85}},{"componentType":"linear","name":"dec2","params":{"inFeatures":85,"outFeatures":2008}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":365500}} {"id":"gen-cnn-2052","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 92 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2052\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":19970}} {"id":"gen-txf-2053","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 391-token sequences with 47 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2053\nComponents:\n - input (input) params={\"shape\":[1,391]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,391]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41177,"embeddingDim":52}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":52,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2154464}} {"id":"gen-gqa-2054","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 70-token sequences with 91 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2054\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,70]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29703,"embeddingDim":44}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[44]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[44]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[44]}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1310936}} {"id":"gen-fix-2055","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (210) is not divisible by numHeads (11). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2055\nComponents:\n - input (input) params={\"shape\":[1,355]}\n - embed (embedding) params={\"numEmbeddings\":45728,\"embeddingDim\":210}\n - attn (multiHeadAttention) params={\"embedDim\":210,\"numHeads\":11}\n - head (linear) params={\"inFeatures\":210,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":9789150}} {"id":"gen-trim-2056","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3179-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2056\nComponents:\n - input (input) params={\"shape\":[1,91]}\n - fc1 (linear) params={\"inFeatures\":91,\"outFeatures\":3179}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3179,\"outFeatures\":3179}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3179,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":91,"outFeatures":81}},{"type":"update_params","name":"fc2","params":{"inFeatures":81,"outFeatures":81}},{"type":"update_params","name":"head","params":{"inFeatures":81,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":16767}} {"id":"gen-norm-2057","family":"norm","seed":20260716,"spec":"This 124-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2057\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - fc1 (linear) params={\"inFeatures\":124,\"outFeatures\":454}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":454,\"outFeatures\":390}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":390,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":454}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":390}}],"grade":{"pass":true,"score":94,"params":246226}} {"id":"gen-tower-2058","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 231-feature user input and a 165-feature item input, each through its own 2-layer MLP tower ending at width 276, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2058\nComponents:\n - user_input (input) params={\"shape\":[1,231]}\n - item_input (input) params={\"shape\":[1,165]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,231]}},{"componentType":"input","name":"item_input","params":{"shape":[1,165]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":231,"outFeatures":276}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":276,"outFeatures":276}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":165,"outFeatures":276}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":276,"outFeatures":276}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":552,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":262200}} {"id":"gen-grow-2059","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (86-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2059\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - fc1 (linear) params={\"inFeatures\":86,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":86,"outFeatures":1132}},{"type":"update_params","name":"fc2","params":{"inFeatures":1132,"outFeatures":1132}},{"type":"update_params","name":"head","params":{"inFeatures":1132,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":1416132}} {"id":"gen-mlp-2060","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 38-feature input with 20 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2060\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,38]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":38,"outFeatures":402}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":402,"outFeatures":192}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":192,"outFeatures":793}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":793,"outFeatures":867}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":867,"outFeatures":773}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":773,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1617898}} {"id":"gen-ae-2061","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1179-dim input: encode down to a 24-dim bottleneck and decode back to 1179. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2061\nComponents:\n - input (input) params={\"shape\":[1,1179]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1179]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1179,"outFeatures":810}},{"componentType":"linear","name":"enc2","params":{"inFeatures":810,"outFeatures":24}},{"componentType":"linear","name":"dec1","params":{"inFeatures":24,"outFeatures":810}},{"componentType":"linear","name":"dec2","params":{"inFeatures":810,"outFeatures":1179}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1948860}} {"id":"gen-cnn-2062","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 62 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2062\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":33,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":59,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":31816}} {"id":"gen-txf-2063","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 258-token sequences with 96 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2063\nComponents:\n - input (input) params={\"shape\":[1,258]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,258]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10914,"embeddingDim":78}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":78,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":78,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":931788}} {"id":"gen-gqa-2064","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 204-token sequences with 33 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2064\nComponents:\n - input (input) params={\"shape\":[1,204]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,204]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33795,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":18943680}} {"id":"gen-trim-2066","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2285-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2066\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":2285}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2285,\"outFeatures\":2285}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2285,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":72,"outFeatures":142}},{"type":"update_params","name":"fc2","params":{"inFeatures":142,"outFeatures":142}},{"type":"update_params","name":"head","params":{"inFeatures":142,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":33938}} {"id":"gen-norm-2067","family":"norm","seed":20260716,"spec":"This 70-feature, 49-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2067\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":251}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":251,\"outFeatures\":444}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":444,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":251}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":444}}],"grade":{"pass":true,"score":94,"params":150770}} {"id":"gen-tower-2068","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 23-feature item input, each through its own 2-layer MLP tower ending at width 381, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2068\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,23]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,23]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":381}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":381,"outFeatures":381}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":23,"outFeatures":381}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":381,"outFeatures":381}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":762,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":391287}} {"id":"gen-grow-2069","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (51-feature input, 13 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2069\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":645}},{"type":"update_params","name":"fc2","params":{"inFeatures":645,"outFeatures":645}},{"type":"update_params","name":"head","params":{"inFeatures":645,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":457305}} {"id":"gen-mlp-2070","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 67-feature input with 148 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2070\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,67]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":67,"outFeatures":604}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":604,"outFeatures":342}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":342,"outFeatures":148}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":297652}} {"id":"gen-ae-2071","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 253-dim input: encode down to a 161-dim bottleneck and decode back to 253. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2071\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,253]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":253,"outFeatures":779}},{"componentType":"linear","name":"enc2","params":{"inFeatures":779,"outFeatures":161}},{"componentType":"linear","name":"dec1","params":{"inFeatures":161,"outFeatures":779}},{"componentType":"linear","name":"dec2","params":{"inFeatures":779,"outFeatures":253}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":645012}} {"id":"gen-cnn-2072","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 100 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2072\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":60,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":58099}} {"id":"gen-txf-2073","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 315-token sequences with 33 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2073\nComponents:\n - input (input) params={\"shape\":[1,315]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,315]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35420,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3773640}} {"id":"gen-gqa-2074","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 38-token sequences with 15 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2074\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,38]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9783,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":224,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2194752}} {"id":"gen-trim-2076","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2707-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2076\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":2707}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2707,\"outFeatures\":2707}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2707,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":206,"outFeatures":147}},{"type":"update_params","name":"fc2","params":{"inFeatures":147,"outFeatures":147}},{"type":"update_params","name":"fc3","params":{"inFeatures":147,"outFeatures":147}},{"type":"update_params","name":"fc4","params":{"inFeatures":147,"outFeatures":147}},{"type":"update_params","name":"head","params":{"inFeatures":147,"outFeatures":48}}],"grade":{"pass":true,"score":88,"params":102165}} {"id":"gen-norm-2077","family":"norm","seed":20260716,"spec":"This 155-feature, 22-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2077\nComponents:\n - input (input) params={\"shape\":[1,155]}\n - fc1 (linear) params={\"inFeatures\":155,\"outFeatures\":430}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":430,\"outFeatures\":104}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":104,\"outFeatures\":40}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":40,\"outFeatures\":142}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":142,\"outFeatures\":276}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":276,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":430}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":104}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":40}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":142}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":276}}],"grade":{"pass":true,"score":100,"params":166474}} {"id":"gen-tower-2078","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 153-feature user input and a 101-feature item input, each through its own 2-layer MLP tower ending at width 482, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2078\nComponents:\n - user_input (input) params={\"shape\":[1,153]}\n - item_input (input) params={\"shape\":[1,101]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,153]}},{"componentType":"input","name":"item_input","params":{"shape":[1,101]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":153,"outFeatures":482}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":482,"outFeatures":482}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":101,"outFeatures":482}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":482,"outFeatures":482}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":964,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":588040}} {"id":"gen-grow-2079","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (107-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2079\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - fc1 (linear) params={\"inFeatures\":107,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":107,"outFeatures":1215}},{"type":"update_params","name":"fc2","params":{"inFeatures":1215,"outFeatures":1215}},{"type":"update_params","name":"head","params":{"inFeatures":1215,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":1635390}} {"id":"gen-mlp-2080","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 487-feature input with 175 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2080\nComponents:\n - input (input) params={\"shape\":[1,487]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,487]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":487,"outFeatures":694}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":694,"outFeatures":454}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":454,"outFeatures":175}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":732504}} {"id":"gen-ae-2081","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1350-dim input: encode down to a 41-dim bottleneck and decode back to 1350. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2081\nComponents:\n - input (input) params={\"shape\":[1,1350]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1350]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1350,"outFeatures":459}},{"componentType":"linear","name":"enc2","params":{"inFeatures":459,"outFeatures":41}},{"componentType":"linear","name":"dec1","params":{"inFeatures":41,"outFeatures":459}},{"componentType":"linear","name":"dec2","params":{"inFeatures":459,"outFeatures":1350}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1276938}} {"id":"gen-cnn-2082","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 5 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2082\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":46,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":31005}} {"id":"gen-txf-2083","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 449-token sequences with 100 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2083\nComponents:\n - input (input) params={\"shape\":[1,449]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,449]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46420,"embeddingDim":312}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":312,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":312,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":14903616}} {"id":"gen-gqa-2084","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 270-token sequences with 10 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2084\nComponents:\n - input (input) params={\"shape\":[1,270]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,270]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38153,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6106080}} {"id":"gen-fix-2085","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (88) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2085\nComponents:\n - input (input) params={\"shape\":[1,259]}\n - embed (embedding) params={\"numEmbeddings\":8084,\"embeddingDim\":88}\n - attn (multiHeadAttention) params={\"embedDim\":88,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":88,\"outFeatures\":83}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":749672}} {"id":"gen-trim-2086","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1585-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2086\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - fc1 (linear) params={\"inFeatures\":134,\"outFeatures\":1585}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1585,\"outFeatures\":1585}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1585,\"outFeatures\":1585}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1585,\"outFeatures\":1585}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1585,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":134,"outFeatures":153}},{"type":"update_params","name":"fc2","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc3","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"fc4","params":{"inFeatures":153,"outFeatures":153}},{"type":"update_params","name":"head","params":{"inFeatures":153,"outFeatures":21}}],"grade":{"pass":true,"score":88,"params":93942}} {"id":"gen-norm-2087","family":"norm","seed":20260716,"spec":"This 19-feature, 50-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2087\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - fc1 (linear) params={\"inFeatures\":19,\"outFeatures\":195}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":195,\"outFeatures\":281}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":281,\"outFeatures\":331}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":331,\"outFeatures\":50}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":50,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":195}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":281}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":331}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":50}}],"grade":{"pass":true,"score":100,"params":170561}} {"id":"gen-tower-2088","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 16-feature user input and a 140-feature item input, each through its own 2-layer MLP tower ending at width 360, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2088\nComponents:\n - user_input (input) params={\"shape\":[1,16]}\n - item_input (input) params={\"shape\":[1,140]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,16]}},{"componentType":"input","name":"item_input","params":{"shape":[1,140]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":16,"outFeatures":360}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":360,"outFeatures":360}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":140,"outFeatures":360}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":360,"outFeatures":360}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":720,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":316080}} {"id":"gen-grow-2089","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (70-feature input, 25 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2089\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":70,"outFeatures":1020}},{"type":"update_params","name":"fc2","params":{"inFeatures":1020,"outFeatures":1020}},{"type":"update_params","name":"head","params":{"inFeatures":1020,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":1137300}} {"id":"gen-mlp-2090","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 349-feature input with 104 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2090\nComponents:\n - input (input) params={\"shape\":[1,349]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,349]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":349,"outFeatures":628}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":628,"outFeatures":626}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":626,"outFeatures":983}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":983,"outFeatures":354}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":354,"outFeatures":446}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":446,"outFeatures":104}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1779908}} {"id":"gen-ae-2091","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 307-dim input: encode down to a 170-dim bottleneck and decode back to 307. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2091\nComponents:\n - input (input) params={\"shape\":[1,307]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,307]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":307,"outFeatures":95}},{"componentType":"linear","name":"enc2","params":{"inFeatures":95,"outFeatures":170}},{"componentType":"linear","name":"dec1","params":{"inFeatures":170,"outFeatures":95}},{"componentType":"linear","name":"dec2","params":{"inFeatures":95,"outFeatures":307}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":90630}} {"id":"gen-cnn-2092","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x48x48 image with 36 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2092\nComponents:\n - input (input) params={\"shape\":[1,3,48,48]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,48,48]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":52,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":15,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":8964}} {"id":"gen-txf-2093","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 409-token sequences with 16 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2093\nComponents:\n - input (input) params={\"shape\":[1,409]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,409]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9331,"embeddingDim":184}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":184,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":184,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1990696}} {"id":"gen-gqa-2094","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 398-token sequences with 71 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2094\nComponents:\n - input (input) params={\"shape\":[1,398]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,398]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43849,"embeddingDim":240}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":240,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[240]}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":10540800}} {"id":"gen-fix-2095","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (214) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2095\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - embed (embedding) params={\"numEmbeddings\":25811,\"embeddingDim\":214}\n - attn (multiHeadAttention) params={\"embedDim\":214,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":214,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5722146}} {"id":"gen-trim-2096","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1638-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2096\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - fc1 (linear) params={\"inFeatures\":100,\"outFeatures\":1638}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1638,\"outFeatures\":1638}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1638,\"outFeatures\":1638}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1638,\"outFeatures\":1638}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1638,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":100,"outFeatures":100}},{"type":"update_params","name":"fc2","params":{"inFeatures":100,"outFeatures":100}},{"type":"update_params","name":"fc3","params":{"inFeatures":100,"outFeatures":100}},{"type":"update_params","name":"fc4","params":{"inFeatures":100,"outFeatures":100}},{"type":"update_params","name":"head","params":{"inFeatures":100,"outFeatures":32}}],"grade":{"pass":true,"score":88,"params":43200}} {"id":"gen-norm-2097","family":"norm","seed":20260716,"spec":"This 69-feature, 76-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2097\nComponents:\n - input (input) params={\"shape\":[1,69]}\n - fc1 (linear) params={\"inFeatures\":69,\"outFeatures\":97}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":97,\"outFeatures\":54}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":54,\"outFeatures\":76}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":97}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":54}}],"grade":{"pass":true,"score":94,"params":16035}} {"id":"gen-tower-2098","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 33-feature user input and a 72-feature item input, each through its own 2-layer MLP tower ending at width 51, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2098\nComponents:\n - user_input (input) params={\"shape\":[1,33]}\n - item_input (input) params={\"shape\":[1,72]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,33]}},{"componentType":"input","name":"item_input","params":{"shape":[1,72]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":33,"outFeatures":51}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":72,"outFeatures":51}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":51,"outFeatures":51}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":102,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":10659}} {"id":"gen-grow-2099","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (45-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2099\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - fc1 (linear) params={\"inFeatures\":45,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":45,"outFeatures":900}},{"type":"update_params","name":"fc2","params":{"inFeatures":900,"outFeatures":900}},{"type":"update_params","name":"head","params":{"inFeatures":900,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":887400}} {"id":"gen-mlp-2100","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 187-feature input with 50 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2100\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,187]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":187,"outFeatures":370}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":370,"outFeatures":473}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":473,"outFeatures":820}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":820,"outFeatures":728}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":728,"outFeatures":619}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":619,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1710602}} {"id":"gen-ae-2101","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 350-dim input: encode down to a 50-dim bottleneck and decode back to 350. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2101\nComponents:\n - input (input) params={\"shape\":[1,350]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,350]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":350,"outFeatures":829}},{"componentType":"linear","name":"enc2","params":{"inFeatures":829,"outFeatures":50}},{"componentType":"linear","name":"dec1","params":{"inFeatures":50,"outFeatures":829}},{"componentType":"linear","name":"dec2","params":{"inFeatures":829,"outFeatures":350}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":663200}} {"id":"gen-cnn-2102","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 34 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2102\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":20,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":9,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":5840}} {"id":"gen-txf-2103","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 309-token sequences with 27 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2103\nComponents:\n - input (input) params={\"shape\":[1,309]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,309]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11242,"embeddingDim":92}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":92,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1070604}} {"id":"gen-gqa-2104","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 279-token sequences with 42 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2104\nComponents:\n - input (input) params={\"shape\":[1,279]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,279]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15039,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1568424}} {"id":"gen-trim-2106","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1620-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2106\nComponents:\n - input (input) params={\"shape\":[1,131]}\n - fc1 (linear) params={\"inFeatures\":131,\"outFeatures\":1620}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1620,\"outFeatures\":1620}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1620,\"outFeatures\":1620}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1620,\"outFeatures\":1620}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1620,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":131,"outFeatures":243}},{"type":"update_params","name":"fc2","params":{"inFeatures":243,"outFeatures":243}},{"type":"update_params","name":"fc3","params":{"inFeatures":243,"outFeatures":243}},{"type":"update_params","name":"fc4","params":{"inFeatures":243,"outFeatures":243}},{"type":"update_params","name":"head","params":{"inFeatures":243,"outFeatures":10}}],"grade":{"pass":true,"score":88,"params":211410}} {"id":"gen-norm-2107","family":"norm","seed":20260716,"spec":"This 122-feature, 37-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2107\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - fc1 (linear) params={\"inFeatures\":122,\"outFeatures\":149}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":149,\"outFeatures\":328}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":328,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":149}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":328}}],"grade":{"pass":true,"score":94,"params":79186}} {"id":"gen-tower-2108","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 147-feature user input and a 193-feature item input, each through its own 2-layer MLP tower ending at width 109, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2108\nComponents:\n - user_input (input) params={\"shape\":[1,147]}\n - item_input (input) params={\"shape\":[1,193]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,147]}},{"componentType":"input","name":"item_input","params":{"shape":[1,193]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":147,"outFeatures":109}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":109,"outFeatures":109}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":193,"outFeatures":109}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":109,"outFeatures":109}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":218,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":61040}} {"id":"gen-grow-2109","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (117-feature input, 23 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2109\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":978}},{"type":"update_params","name":"fc2","params":{"inFeatures":978,"outFeatures":978}},{"type":"update_params","name":"head","params":{"inFeatures":978,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":1093404}} {"id":"gen-mlp-2110","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 421-feature input with 56 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2110\nComponents:\n - input (input) params={\"shape\":[1,421]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,421]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":421,"outFeatures":506}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":506,"outFeatures":677}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":677,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":593500}} {"id":"gen-ae-2111","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1122-dim input: encode down to a 213-dim bottleneck and decode back to 1122. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2111\nComponents:\n - input (input) params={\"shape\":[1,1122]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1122]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1122,"outFeatures":431}},{"componentType":"linear","name":"enc2","params":{"inFeatures":431,"outFeatures":213}},{"componentType":"linear","name":"dec1","params":{"inFeatures":213,"outFeatures":431}},{"componentType":"linear","name":"dec2","params":{"inFeatures":431,"outFeatures":1122}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1150770}} {"id":"gen-cnn-2112","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 77 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2112\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":45,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":27,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":31030}} {"id":"gen-txf-2113","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 182-token sequences with 67 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2113\nComponents:\n - input (input) params={\"shape\":[1,182]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,182]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33278,"embeddingDim":192}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":192,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6549696}} {"id":"gen-gqa-2114","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 302-token sequences with 15 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2114\nComponents:\n - input (input) params={\"shape\":[1,302]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,302]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30356,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":11176528}} {"id":"gen-trim-2116","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1791-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2116\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":1791}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1791,\"outFeatures\":1791}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1791,\"outFeatures\":1791}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1791,\"outFeatures\":1791}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1791,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":80,"outFeatures":259}},{"type":"update_params","name":"fc2","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"fc3","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"fc4","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"head","params":{"inFeatures":259,"outFeatures":43}}],"grade":{"pass":true,"score":88,"params":233100}} {"id":"gen-norm-2117","family":"norm","seed":20260716,"spec":"This 49-feature, 100-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2117\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":426}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":426,\"outFeatures\":170}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":170,\"outFeatures\":316}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":316,\"outFeatures\":100}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":426}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":170}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":316}}],"grade":{"pass":true,"score":100,"params":178614}} {"id":"gen-tower-2118","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 217-feature user input and a 200-feature item input, each through its own 2-layer MLP tower ending at width 239, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2118\nComponents:\n - user_input (input) params={\"shape\":[1,217]}\n - item_input (input) params={\"shape\":[1,200]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,217]}},{"componentType":"input","name":"item_input","params":{"shape":[1,200]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":217,"outFeatures":239}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":239,"outFeatures":239}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":200,"outFeatures":239}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":239,"outFeatures":239}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":478,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":214383}} {"id":"gen-grow-2119","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (116-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2119\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":1267}},{"type":"update_params","name":"fc2","params":{"inFeatures":1267,"outFeatures":1267}},{"type":"update_params","name":"head","params":{"inFeatures":1267,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":1814344}} {"id":"gen-mlp-2120","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 425-feature input with 98 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2120\nComponents:\n - input (input) params={\"shape\":[1,425]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,425]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":425,"outFeatures":282}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":282,"outFeatures":488}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":488,"outFeatures":503}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":503,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":552224}} {"id":"gen-ae-2121","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1799-dim input: encode down to a 91-dim bottleneck and decode back to 1799. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2121\nComponents:\n - input (input) params={\"shape\":[1,1799]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1799]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1799,"outFeatures":202}},{"componentType":"linear","name":"enc2","params":{"inFeatures":202,"outFeatures":91}},{"componentType":"linear","name":"dec1","params":{"inFeatures":91,"outFeatures":202}},{"componentType":"linear","name":"dec2","params":{"inFeatures":202,"outFeatures":1799}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":763560}} {"id":"gen-cnn-2122","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x25x25 image with 23 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2122\nComponents:\n - input (input) params={\"shape\":[1,3,25,25]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,25,25]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":45113}} {"id":"gen-txf-2123","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 83-token sequences with 72 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2123\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,83]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48368,"embeddingDim":52}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":52,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2529696}} {"id":"gen-gqa-2124","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 56-token sequences with 59 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2124\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,56]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9356,"embeddingDim":120}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":120,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[120]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":120,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[120]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":120,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[120]}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1129800}} {"id":"gen-trim-2126","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2864-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2126\nComponents:\n - input (input) params={\"shape\":[1,40]}\n - fc1 (linear) params={\"inFeatures\":40,\"outFeatures\":2864}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2864,\"outFeatures\":2864}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2864,\"outFeatures\":2864}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2864,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":40,"outFeatures":64}},{"type":"update_params","name":"fc2","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"fc3","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"head","params":{"inFeatures":64,"outFeatures":27}}],"grade":{"pass":true,"score":84,"params":12480}} {"id":"gen-norm-2127","family":"norm","seed":20260716,"spec":"This 217-feature, 68-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2127\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":187}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":187,\"outFeatures\":333}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":333,\"outFeatures\":242}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":242,\"outFeatures\":95}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":95,\"outFeatures\":139}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":139,\"outFeatures\":68}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":187}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":333}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":242}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":95}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":139}}],"grade":{"pass":true,"score":100,"params":229083}} {"id":"gen-tower-2128","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 48-feature user input and a 198-feature item input, each through its own 2-layer MLP tower ending at width 408, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2128\nComponents:\n - user_input (input) params={\"shape\":[1,48]}\n - item_input (input) params={\"shape\":[1,198]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,48]}},{"componentType":"input","name":"item_input","params":{"shape":[1,198]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":48,"outFeatures":408}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":408,"outFeatures":408}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":198,"outFeatures":408}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":408,"outFeatures":408}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":816,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":434112}} {"id":"gen-mlp-2130","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 173-feature input with 145 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2130\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,173]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":173,"outFeatures":892}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":892,"outFeatures":39}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":39,"outFeatures":641}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":641,"outFeatures":145}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":307048}} {"id":"gen-ae-2131","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1492-dim input: encode down to a 42-dim bottleneck and decode back to 1492. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2131\nComponents:\n - input (input) params={\"shape\":[1,1492]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1492]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1492,"outFeatures":261}},{"componentType":"linear","name":"enc2","params":{"inFeatures":261,"outFeatures":42}},{"componentType":"linear","name":"dec1","params":{"inFeatures":42,"outFeatures":261}},{"componentType":"linear","name":"dec2","params":{"inFeatures":261,"outFeatures":1492}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":800748}} {"id":"gen-cnn-2132","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 26 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2132\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":21,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":22,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":17717}} {"id":"gen-txf-2133","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 229-token sequences with 86 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2133\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,229]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47187,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":48,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2287536}} {"id":"gen-gqa-2134","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 217-token sequences with 36 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2134\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,217]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22033,"embeddingDim":88}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":88,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[88]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":88,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[88]}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":1942072}} {"id":"gen-trim-2136","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3078-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2136\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - fc1 (linear) params={\"inFeatures\":240,\"outFeatures\":3078}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3078,\"outFeatures\":3078}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3078,\"outFeatures\":3078}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3078,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":240,"outFeatures":163}},{"type":"update_params","name":"fc2","params":{"inFeatures":163,"outFeatures":163}},{"type":"update_params","name":"fc3","params":{"inFeatures":163,"outFeatures":163}},{"type":"update_params","name":"head","params":{"inFeatures":163,"outFeatures":10}}],"grade":{"pass":true,"score":84,"params":93888}} {"id":"gen-norm-2137","family":"norm","seed":20260716,"spec":"This 172-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2137\nComponents:\n - input (input) params={\"shape\":[1,172]}\n - fc1 (linear) params={\"inFeatures\":172,\"outFeatures\":474}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":474,\"outFeatures\":51}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":51,\"outFeatures\":391}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":391,\"outFeatures\":337}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":337,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":474}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":51}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":391}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":337}}],"grade":{"pass":true,"score":100,"params":267520}} {"id":"gen-tower-2138","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 168-feature user input and a 39-feature item input, each through its own 2-layer MLP tower ending at width 97, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2138\nComponents:\n - user_input (input) params={\"shape\":[1,168]}\n - item_input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,168]}},{"componentType":"input","name":"item_input","params":{"shape":[1,39]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":168,"outFeatures":97}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":97,"outFeatures":97}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":39,"outFeatures":97}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":97,"outFeatures":97}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":194,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39091}} {"id":"gen-grow-2139","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (52-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2139\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - fc1 (linear) params={\"inFeatures\":52,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":52,"outFeatures":793}},{"type":"update_params","name":"fc2","params":{"inFeatures":793,"outFeatures":793}},{"type":"update_params","name":"head","params":{"inFeatures":793,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":692289}} {"id":"gen-mlp-2140","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 71-feature input with 22 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2140\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,71]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":71,"outFeatures":725}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":725,"outFeatures":616}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":616,"outFeatures":981}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":981,"outFeatures":364}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":364,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1467463}} {"id":"gen-ae-2141","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1845-dim input: encode down to a 170-dim bottleneck and decode back to 1845. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2141\nComponents:\n - input (input) params={\"shape\":[1,1845]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1845]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1845,"outFeatures":357}},{"componentType":"linear","name":"enc2","params":{"inFeatures":357,"outFeatures":170}},{"componentType":"linear","name":"dec1","params":{"inFeatures":170,"outFeatures":357}},{"componentType":"linear","name":"dec2","params":{"inFeatures":357,"outFeatures":1845}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1438710}} {"id":"gen-cnn-2142","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 66 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2142\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":28,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":21,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":56,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":41712}} {"id":"gen-txf-2143","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 180-token sequences with 39 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2143\nComponents:\n - input (input) params={\"shape\":[1,180]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,180]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45307,"embeddingDim":352}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":352,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":16457408}} {"id":"gen-gqa-2144","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 261-token sequences with 10 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2144\nComponents:\n - input (input) params={\"shape\":[1,261]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,261]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24036,"embeddingDim":576}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":576,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[576]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":576,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[576]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":576,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[576]}},{"componentType":"linear","name":"head","params":{"inFeatures":576,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":13850496}} {"id":"gen-fix-2145","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (392) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2145\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - embed (embedding) params={\"numEmbeddings\":49962,\"embeddingDim\":392}\n - attn (multiHeadAttention) params={\"embedDim\":392,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":392,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":20200936}} {"id":"gen-trim-2146","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3389-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2146\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":3389}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3389,\"outFeatures\":3389}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3389,\"outFeatures\":3389}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3389,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":41,"outFeatures":384}},{"type":"update_params","name":"fc2","params":{"inFeatures":384,"outFeatures":384}},{"type":"update_params","name":"fc3","params":{"inFeatures":384,"outFeatures":384}},{"type":"update_params","name":"head","params":{"inFeatures":384,"outFeatures":3}}],"grade":{"pass":true,"score":84,"params":311808}} {"id":"gen-norm-2147","family":"norm","seed":20260716,"spec":"This 227-feature, 76-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2147\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - fc1 (linear) params={\"inFeatures\":227,\"outFeatures\":270}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":270,\"outFeatures\":464}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":464,\"outFeatures\":253}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":253,\"outFeatures\":76}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":270}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":464}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":253}}],"grade":{"pass":true,"score":100,"params":323190}} {"id":"gen-tower-2148","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 39-feature user input and a 19-feature item input, each through its own 2-layer MLP tower ending at width 136, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2148\nComponents:\n - user_input (input) params={\"shape\":[1,39]}\n - item_input (input) params={\"shape\":[1,19]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,39]}},{"componentType":"input","name":"item_input","params":{"shape":[1,19]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":39,"outFeatures":136}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":136,"outFeatures":136}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":19,"outFeatures":136}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":136,"outFeatures":136}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":45152}} {"id":"gen-grow-2149","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 25 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2149\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":1027}},{"type":"update_params","name":"fc2","params":{"inFeatures":1027,"outFeatures":1027}},{"type":"update_params","name":"head","params":{"inFeatures":1027,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":1182077}} {"id":"gen-mlp-2150","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 328-feature input with 67 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2150\nComponents:\n - input (input) params={\"shape\":[1,328]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,328]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":328,"outFeatures":426}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":426,"outFeatures":45}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":45,"outFeatures":989}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":989,"outFeatures":292}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":292,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":511755}} {"id":"gen-ae-2151","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 826-dim input: encode down to a 120-dim bottleneck and decode back to 826. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2151\nComponents:\n - input (input) params={\"shape\":[1,826]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,826]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":826,"outFeatures":263}},{"componentType":"linear","name":"enc2","params":{"inFeatures":263,"outFeatures":120}},{"componentType":"linear","name":"dec1","params":{"inFeatures":120,"outFeatures":263}},{"componentType":"linear","name":"dec2","params":{"inFeatures":263,"outFeatures":826}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":497596}} {"id":"gen-cnn-2152","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 34 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2152\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":12,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":16240}} {"id":"gen-txf-2153","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 38-token sequences with 21 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2153\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,38]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45387,"embeddingDim":98}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":98,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":98,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":98,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":98,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4565232}} {"id":"gen-gqa-2154","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 490-token sequences with 6 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2154\nComponents:\n - input (input) params={\"shape\":[1,490]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,490]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45216,"embeddingDim":140}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":140,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[140]}},{"componentType":"linear","name":"head","params":{"inFeatures":140,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":6331080}} {"id":"gen-trim-2156","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3882-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2156\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":3882}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3882,\"outFeatures\":3882}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3882,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":67}},{"type":"update_params","name":"fc2","params":{"inFeatures":67,"outFeatures":67}},{"type":"update_params","name":"head","params":{"inFeatures":67,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":8442}} {"id":"gen-norm-2157","family":"norm","seed":20260716,"spec":"This 226-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2157\nComponents:\n - input (input) params={\"shape\":[1,226]}\n - fc1 (linear) params={\"inFeatures\":226,\"outFeatures\":112}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":112,\"outFeatures\":350}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":350,\"outFeatures\":456}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":456,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":112}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":350}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":456}}],"grade":{"pass":true,"score":100,"params":254664}} {"id":"gen-tower-2158","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 217-feature user input and a 178-feature item input, each through its own 2-layer MLP tower ending at width 94, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2158\nComponents:\n - user_input (input) params={\"shape\":[1,217]}\n - item_input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,217]}},{"componentType":"input","name":"item_input","params":{"shape":[1,178]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":217,"outFeatures":94}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":178,"outFeatures":94}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":188,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":54990}} {"id":"gen-grow-2159","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (79-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2159\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - fc1 (linear) params={\"inFeatures\":79,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":79,"outFeatures":1065}},{"type":"update_params","name":"fc2","params":{"inFeatures":1065,"outFeatures":1065}},{"type":"update_params","name":"head","params":{"inFeatures":1065,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":1235400}} {"id":"gen-mlp-2160","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 308-feature input with 140 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2160\nComponents:\n - input (input) params={\"shape\":[1,308]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,308]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":308,"outFeatures":474}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":474,"outFeatures":323}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":323,"outFeatures":303}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":303,"outFeatures":686}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":686,"outFeatures":250}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":250,"outFeatures":140}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":811321}} {"id":"gen-ae-2161","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1414-dim input: encode down to a 256-dim bottleneck and decode back to 1414. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2161\nComponents:\n - input (input) params={\"shape\":[1,1414]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1414]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1414,"outFeatures":190}},{"componentType":"linear","name":"enc2","params":{"inFeatures":190,"outFeatures":256}},{"componentType":"linear","name":"dec1","params":{"inFeatures":256,"outFeatures":190}},{"componentType":"linear","name":"dec2","params":{"inFeatures":190,"outFeatures":1414}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":634600}} {"id":"gen-cnn-2162","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x38x38 image with 100 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2162\nComponents:\n - input (input) params={\"shape\":[1,3,38,38]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,38,38]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":35,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":60,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":19,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":34480}} {"id":"gen-txf-2163","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 282-token sequences with 38 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2163\nComponents:\n - input (input) params={\"shape\":[1,282]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,282]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14252,"embeddingDim":172}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":172,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":172,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":172,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2812888}} {"id":"gen-gqa-2164","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 382-token sequences with 32 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2164\nComponents:\n - input (input) params={\"shape\":[1,382]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,382]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42909,"embeddingDim":68}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":68,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[68]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":68,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[68]}},{"componentType":"linear","name":"head","params":{"inFeatures":68,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2919988}} {"id":"gen-fix-2165","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (72) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2165\nComponents:\n - input (input) params={\"shape\":[1,252]}\n - embed (embedding) params={\"numEmbeddings\":37225,\"embeddingDim\":72}\n - attn (multiHeadAttention) params={\"embedDim\":72,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":72,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":2706768}} {"id":"gen-trim-2166","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 4040-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2166\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - fc1 (linear) params={\"inFeatures\":63,\"outFeatures\":4040}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4040,\"outFeatures\":4040}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4040,\"outFeatures\":4040}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":4040,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":63,"outFeatures":250}},{"type":"update_params","name":"fc2","params":{"inFeatures":250,"outFeatures":250}},{"type":"update_params","name":"fc3","params":{"inFeatures":250,"outFeatures":250}},{"type":"update_params","name":"head","params":{"inFeatures":250,"outFeatures":15}}],"grade":{"pass":true,"score":84,"params":144500}} {"id":"gen-norm-2167","family":"norm","seed":20260716,"spec":"This 159-feature, 17-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2167\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":161}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":161,\"outFeatures\":447}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":447,\"outFeatures\":402}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":402,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":161}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":447}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":402}}],"grade":{"pass":true,"score":100,"params":284094}} {"id":"gen-tower-2168","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 170-feature user input and a 38-feature item input, each through its own 2-layer MLP tower ending at width 441, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2168\nComponents:\n - user_input (input) params={\"shape\":[1,170]}\n - item_input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,170]}},{"componentType":"input","name":"item_input","params":{"shape":[1,38]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":170,"outFeatures":441}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":441,"outFeatures":441}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":38,"outFeatures":441}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":441,"outFeatures":441}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":882,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":481572}} {"id":"gen-grow-2169","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (118-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2169\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":118,"outFeatures":1119}},{"type":"update_params","name":"fc2","params":{"inFeatures":1119,"outFeatures":1119}},{"type":"update_params","name":"head","params":{"inFeatures":1119,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":1426725}} {"id":"gen-mlp-2170","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 487-feature input with 42 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2170\nComponents:\n - input (input) params={\"shape\":[1,487]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,487]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":487,"outFeatures":632}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":632,"outFeatures":69}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":69,"outFeatures":854}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":854,"outFeatures":918}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":918,"outFeatures":766}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":766,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1929650}} {"id":"gen-ae-2171","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 619-dim input: encode down to a 191-dim bottleneck and decode back to 619. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2171\nComponents:\n - input (input) params={\"shape\":[1,619]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,619]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":619,"outFeatures":586}},{"componentType":"linear","name":"enc2","params":{"inFeatures":586,"outFeatures":191}},{"componentType":"linear","name":"dec1","params":{"inFeatures":191,"outFeatures":586}},{"componentType":"linear","name":"dec2","params":{"inFeatures":586,"outFeatures":619}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":949320}} {"id":"gen-cnn-2172","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 95 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2172\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":12,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":95}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30755}} {"id":"gen-txf-2173","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 151-token sequences with 82 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2173\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,151]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8842,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":116,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1142832}} {"id":"gen-gqa-2174","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 315-token sequences with 91 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2174\nComponents:\n - input (input) params={\"shape\":[1,315]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,315]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35654,"embeddingDim":84}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":84,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[84]}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3002580}} {"id":"gen-trim-2176","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1989-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2176\nComponents:\n - input (input) params={\"shape\":[1,193]}\n - fc1 (linear) params={\"inFeatures\":193,\"outFeatures\":1989}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1989,\"outFeatures\":1989}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1989,\"outFeatures\":1989}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1989,\"outFeatures\":1989}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1989,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":193,"outFeatures":129}},{"type":"update_params","name":"fc2","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"fc3","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"fc4","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"head","params":{"inFeatures":129,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":77013}} {"id":"gen-norm-2177","family":"norm","seed":20260716,"spec":"This 146-feature, 4-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2177\nComponents:\n - input (input) params={\"shape\":[1,146]}\n - fc1 (linear) params={\"inFeatures\":146,\"outFeatures\":407}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":407,\"outFeatures\":415}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":415,\"outFeatures\":239}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":239,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":407}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":415}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":239}}],"grade":{"pass":true,"score":100,"params":328468}} {"id":"gen-tower-2178","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 209-feature user input and a 88-feature item input, each through its own 2-layer MLP tower ending at width 310, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2178\nComponents:\n - user_input (input) params={\"shape\":[1,209]}\n - item_input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,209]}},{"componentType":"input","name":"item_input","params":{"shape":[1,88]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":209,"outFeatures":310}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":310,"outFeatures":310}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":88,"outFeatures":310}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":310,"outFeatures":310}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":620,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":284890}} {"id":"gen-grow-2179","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (64-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2179\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":64,"outFeatures":830}},{"type":"update_params","name":"fc2","params":{"inFeatures":830,"outFeatures":830}},{"type":"update_params","name":"head","params":{"inFeatures":830,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":771070}} {"id":"gen-mlp-2180","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 164-feature input with 71 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2180\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,164]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":164,"outFeatures":108}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":108,"outFeatures":73}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":73,"outFeatures":464}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":464,"outFeatures":233}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":233,"outFeatures":524}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":524,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":326876}} {"id":"gen-ae-2181","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 254-dim input: encode down to a 239-dim bottleneck and decode back to 254. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2181\nComponents:\n - input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,254]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":254,"outFeatures":731}},{"componentType":"linear","name":"enc2","params":{"inFeatures":731,"outFeatures":239}},{"componentType":"linear","name":"dec1","params":{"inFeatures":239,"outFeatures":731}},{"componentType":"linear","name":"dec2","params":{"inFeatures":731,"outFeatures":254}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":720766}} {"id":"gen-cnn-2182","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x26x26 image with 72 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2182\nComponents:\n - input (input) params={\"shape\":[1,3,26,26]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,26,26]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9234}} {"id":"gen-txf-2183","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 178-token sequences with 36 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2183\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,178]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28712,"embeddingDim":54}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":54,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":36}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1564056}} {"id":"gen-gqa-2184","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 126-token sequences with 51 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2184\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,126]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37599,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":104,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3915600}} {"id":"gen-fix-2185","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (366) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2185\nComponents:\n - input (input) params={\"shape\":[1,320]}\n - embed (embedding) params={\"numEmbeddings\":20637,\"embeddingDim\":366}\n - attn (multiHeadAttention) params={\"embedDim\":366,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":366,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":8111292}} {"id":"gen-trim-2186","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3440-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2186\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":3440}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3440,\"outFeatures\":3440}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3440,\"outFeatures\":3440}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3440,\"outFeatures\":3440}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3440,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":237}},{"type":"update_params","name":"fc2","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"fc3","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"fc4","params":{"inFeatures":237,"outFeatures":237}},{"type":"update_params","name":"head","params":{"inFeatures":237,"outFeatures":44}}],"grade":{"pass":true,"score":88,"params":189363}} {"id":"gen-norm-2187","family":"norm","seed":20260716,"spec":"This 191-feature, 70-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2187\nComponents:\n - input (input) params={\"shape\":[1,191]}\n - fc1 (linear) params={\"inFeatures\":191,\"outFeatures\":250}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":250,\"outFeatures\":416}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":416,\"outFeatures\":168}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":168,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":250}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":416}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":168}}],"grade":{"pass":true,"score":100,"params":233398}} {"id":"gen-tower-2188","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 63-feature user input and a 199-feature item input, each through its own 2-layer MLP tower ending at width 367, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2188\nComponents:\n - user_input (input) params={\"shape\":[1,63]}\n - item_input (input) params={\"shape\":[1,199]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,63]}},{"componentType":"input","name":"item_input","params":{"shape":[1,199]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":63,"outFeatures":367}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":367,"outFeatures":367}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":199,"outFeatures":367}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":367,"outFeatures":367}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":734,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":366266}} {"id":"gen-grow-2189","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (29-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2189\nComponents:\n - input (input) params={\"shape\":[1,29]}\n - fc1 (linear) params={\"inFeatures\":29,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":29,"outFeatures":1393}},{"type":"update_params","name":"fc2","params":{"inFeatures":1393,"outFeatures":1393}},{"type":"update_params","name":"head","params":{"inFeatures":1393,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":2017064}} {"id":"gen-mlp-2190","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 472-feature input with 175 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2190\nComponents:\n - input (input) params={\"shape\":[1,472]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,472]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":472,"outFeatures":145}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":145,"outFeatures":1004}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1004,"outFeatures":287}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":287,"outFeatures":710}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":710,"outFeatures":588}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":588,"outFeatures":175}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1226318}} {"id":"gen-ae-2191","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1540-dim input: encode down to a 50-dim bottleneck and decode back to 1540. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2191\nComponents:\n - input (input) params={\"shape\":[1,1540]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1540]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1540,"outFeatures":229}},{"componentType":"linear","name":"enc2","params":{"inFeatures":229,"outFeatures":50}},{"componentType":"linear","name":"dec1","params":{"inFeatures":50,"outFeatures":229}},{"componentType":"linear","name":"dec2","params":{"inFeatures":229,"outFeatures":1540}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":728220}} {"id":"gen-cnn-2192","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 13 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2192\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":44059}} {"id":"gen-txf-2193","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 267-token sequences with 98 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2193\nComponents:\n - input (input) params={\"shape\":[1,267]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,267]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38524,"embeddingDim":184}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":184,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":7241872}} {"id":"gen-gqa-2194","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 283-token sequences with 73 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2194\nComponents:\n - input (input) params={\"shape\":[1,283]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,283]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30976,"embeddingDim":544}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":544,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[544]}},{"componentType":"linear","name":"head","params":{"inFeatures":544,"outFeatures":73}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":16890656}} {"id":"gen-fix-2195","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (228) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2195\nComponents:\n - input (input) params={\"shape\":[1,496]}\n - embed (embedding) params={\"numEmbeddings\":28899,\"embeddingDim\":228}\n - attn (multiHeadAttention) params={\"embedDim\":228,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":228,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":6813552}} {"id":"gen-trim-2196","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2503-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2196\nComponents:\n - input (input) params={\"shape\":[1,203]}\n - fc1 (linear) params={\"inFeatures\":203,\"outFeatures\":2503}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2503,\"outFeatures\":2503}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2503,\"outFeatures\":2503}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2503,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":203,"outFeatures":360}},{"type":"update_params","name":"fc2","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"fc3","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"head","params":{"inFeatures":360,"outFeatures":13}}],"grade":{"pass":true,"score":84,"params":336960}} {"id":"gen-norm-2197","family":"norm","seed":20260716,"spec":"This 98-feature, 68-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2197\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":189}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":189,\"outFeatures\":163}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":163,\"outFeatures\":68}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":189}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":163}}],"grade":{"pass":true,"score":94,"params":60413}} {"id":"gen-tower-2198","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 214-feature user input and a 132-feature item input, each through its own 2-layer MLP tower ending at width 117, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2198\nComponents:\n - user_input (input) params={\"shape\":[1,214]}\n - item_input (input) params={\"shape\":[1,132]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,214]}},{"componentType":"input","name":"item_input","params":{"shape":[1,132]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":214,"outFeatures":117}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":117,"outFeatures":117}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":132,"outFeatures":117}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":117,"outFeatures":117}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":234,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":68094}} {"id":"gen-grow-2199","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (35-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2199\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - fc1 (linear) params={\"inFeatures\":35,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":35,"outFeatures":1268}},{"type":"update_params","name":"fc2","params":{"inFeatures":1268,"outFeatures":1268}},{"type":"update_params","name":"head","params":{"inFeatures":1268,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":1714336}} {"id":"gen-mlp-2200","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 274-feature input with 101 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2200\nComponents:\n - input (input) params={\"shape\":[1,274]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,274]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":274,"outFeatures":303}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":303,"outFeatures":940}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":940,"outFeatures":408}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":408,"outFeatures":101}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":792570}} {"id":"gen-ae-2201","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1514-dim input: encode down to a 148-dim bottleneck and decode back to 1514. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2201\nComponents:\n - input (input) params={\"shape\":[1,1514]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1514]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1514,"outFeatures":341}},{"componentType":"linear","name":"enc2","params":{"inFeatures":341,"outFeatures":148}},{"componentType":"linear","name":"dec1","params":{"inFeatures":148,"outFeatures":341}},{"componentType":"linear","name":"dec2","params":{"inFeatures":341,"outFeatures":1514}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1133484}} {"id":"gen-cnn-2202","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 3 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2202\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":63,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9099}} {"id":"gen-txf-2203","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 61-token sequences with 32 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2203\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,61]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42502,"embeddingDim":52}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":52,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":52,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":52,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2244216}} {"id":"gen-gqa-2204","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 335-token sequences with 9 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2204\nComponents:\n - input (input) params={\"shape\":[1,335]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,335]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36252,"embeddingDim":376}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":376,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[376]}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":13634136}} {"id":"gen-trim-2206","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3943-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2206\nComponents:\n - input (input) params={\"shape\":[1,37]}\n - fc1 (linear) params={\"inFeatures\":37,\"outFeatures\":3943}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3943,\"outFeatures\":3943}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3943,\"outFeatures\":3943}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3943,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":37,"outFeatures":357}},{"type":"update_params","name":"fc2","params":{"inFeatures":357,"outFeatures":357}},{"type":"update_params","name":"fc3","params":{"inFeatures":357,"outFeatures":357}},{"type":"update_params","name":"head","params":{"inFeatures":357,"outFeatures":31}}],"grade":{"pass":true,"score":84,"params":279174}} {"id":"gen-norm-2207","family":"norm","seed":20260716,"spec":"This 110-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2207\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":85}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":85,\"outFeatures\":277}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":277,\"outFeatures\":34}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":34,\"outFeatures\":279}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":279,\"outFeatures\":109}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":109,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":85}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":277}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":34}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":279}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":109}}],"grade":{"pass":true,"score":100,"params":87987}} {"id":"gen-tower-2208","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 201-feature user input and a 187-feature item input, each through its own 2-layer MLP tower ending at width 490, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2208\nComponents:\n - user_input (input) params={\"shape\":[1,201]}\n - item_input (input) params={\"shape\":[1,187]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,201]}},{"componentType":"input","name":"item_input","params":{"shape":[1,187]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":201,"outFeatures":490}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":490,"outFeatures":490}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":187,"outFeatures":490}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":490,"outFeatures":490}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":980,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":671300}} {"id":"gen-grow-2209","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (41-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2209\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - fc1 (linear) params={\"inFeatures\":41,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":41,"outFeatures":711}},{"type":"update_params","name":"fc2","params":{"inFeatures":711,"outFeatures":711}},{"type":"update_params","name":"head","params":{"inFeatures":711,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":536805}} {"id":"gen-mlp-2210","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 422-feature input with 68 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2210\nComponents:\n - input (input) params={\"shape\":[1,422]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,422]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":422,"outFeatures":164}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":164,"outFeatures":662}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":662,"outFeatures":342}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":342,"outFeatures":1005}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1005,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":816230}} {"id":"gen-ae-2211","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1732-dim input: encode down to a 93-dim bottleneck and decode back to 1732. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2211\nComponents:\n - input (input) params={\"shape\":[1,1732]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1732]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1732,"outFeatures":976}},{"componentType":"linear","name":"enc2","params":{"inFeatures":976,"outFeatures":93}},{"componentType":"linear","name":"dec1","params":{"inFeatures":93,"outFeatures":976}},{"componentType":"linear","name":"dec2","params":{"inFeatures":976,"outFeatures":1732}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3562400}} {"id":"gen-cnn-2212","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x51x51 image with 100 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2212\nComponents:\n - input (input) params={\"shape\":[1,3,51,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,51,51]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":28,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":47,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":38,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36630}} {"id":"gen-txf-2213","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 461-token sequences with 40 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2213\nComponents:\n - input (input) params={\"shape\":[1,461]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,461]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47873,"embeddingDim":60}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":60,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":60,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2903580}} {"id":"gen-gqa-2214","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 85-token sequences with 57 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2214\nComponents:\n - input (input) params={\"shape\":[1,85]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,85]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38775,"embeddingDim":88}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":88,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[88]}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3417216}} {"id":"gen-trim-2216","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2437-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2216\nComponents:\n - input (input) params={\"shape\":[1,203]}\n - fc1 (linear) params={\"inFeatures\":203,\"outFeatures\":2437}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2437,\"outFeatures\":2437}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2437,\"outFeatures\":2437}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2437,\"outFeatures\":2437}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2437,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":203,"outFeatures":256}},{"type":"update_params","name":"fc2","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"fc3","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"fc4","params":{"inFeatures":256,"outFeatures":256}},{"type":"update_params","name":"head","params":{"inFeatures":256,"outFeatures":13}}],"grade":{"pass":true,"score":88,"params":251904}} {"id":"gen-norm-2217","family":"norm","seed":20260716,"spec":"This 16-feature, 7-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2217\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":329}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":329,\"outFeatures\":71}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":71,\"outFeatures\":339}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":339,\"outFeatures\":52}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":52,\"outFeatures\":250}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":250,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":329}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":71}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":339}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":52}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":250}}],"grade":{"pass":true,"score":100,"params":85070}} {"id":"gen-tower-2218","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 204-feature user input and a 252-feature item input, each through its own 2-layer MLP tower ending at width 124, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2218\nComponents:\n - user_input (input) params={\"shape\":[1,204]}\n - item_input (input) params={\"shape\":[1,252]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,204]}},{"componentType":"input","name":"item_input","params":{"shape":[1,252]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":204,"outFeatures":124}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":124,"outFeatures":124}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":252,"outFeatures":124}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":124,"outFeatures":124}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":248,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":87544}} {"id":"gen-grow-2219","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (38-feature input, 40 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2219\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":729}},{"type":"update_params","name":"fc2","params":{"inFeatures":729,"outFeatures":729}},{"type":"update_params","name":"head","params":{"inFeatures":729,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":588303}} {"id":"gen-mlp-2220","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 214-feature input with 129 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2220\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,214]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":214,"outFeatures":596}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":596,"outFeatures":974}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":974,"outFeatures":129}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":833694}} {"id":"gen-ae-2221","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1784-dim input: encode down to a 38-dim bottleneck and decode back to 1784. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2221\nComponents:\n - input (input) params={\"shape\":[1,1784]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1784]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1784,"outFeatures":420}},{"componentType":"linear","name":"enc2","params":{"inFeatures":420,"outFeatures":38}},{"componentType":"linear","name":"dec1","params":{"inFeatures":38,"outFeatures":420}},{"componentType":"linear","name":"dec2","params":{"inFeatures":420,"outFeatures":1784}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1530480}} {"id":"gen-cnn-2222","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 4 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2222\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":55,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":9,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":49,"outFeatures":4}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":36754}} {"id":"gen-txf-2223","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 179-token sequences with 66 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2223\nComponents:\n - input (input) params={\"shape\":[1,179]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,179]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46629,"embeddingDim":98}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":98,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":98,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4614526}} {"id":"gen-gqa-2224","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 123-token sequences with 66 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2224\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,123]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36357,"embeddingDim":168}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":168,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[168]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":168,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[168]}},{"componentType":"linear","name":"head","params":{"inFeatures":168,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6119064}} {"id":"gen-trim-2226","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3342-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2226\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":3342}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3342,\"outFeatures\":3342}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3342,\"outFeatures\":3342}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3342,\"outFeatures\":3342}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3342,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":145,"outFeatures":68}},{"type":"update_params","name":"fc2","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"fc3","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"fc4","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"head","params":{"inFeatures":68,"outFeatures":16}}],"grade":{"pass":true,"score":88,"params":24820}} {"id":"gen-norm-2227","family":"norm","seed":20260716,"spec":"This 53-feature, 50-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2227\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":496}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":496,\"outFeatures\":300}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":300,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":496}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":300}}],"grade":{"pass":true,"score":94,"params":190088}} {"id":"gen-tower-2228","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 81-feature user input and a 174-feature item input, each through its own 2-layer MLP tower ending at width 290, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2228\nComponents:\n - user_input (input) params={\"shape\":[1,81]}\n - item_input (input) params={\"shape\":[1,174]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,81]}},{"componentType":"input","name":"item_input","params":{"shape":[1,174]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":81,"outFeatures":290}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":290,"outFeatures":290}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":174,"outFeatures":290}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":290,"outFeatures":290}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":580,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":242730}} {"id":"gen-grow-2229","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (114-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2229\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":1156}},{"type":"update_params","name":"fc2","params":{"inFeatures":1156,"outFeatures":1156}},{"type":"update_params","name":"head","params":{"inFeatures":1156,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":1495864}} {"id":"gen-mlp-2230","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 171-feature input with 188 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2230\nComponents:\n - input (input) params={\"shape\":[1,171]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,171]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":171,"outFeatures":204}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":204,"outFeatures":791}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":791,"outFeatures":412}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":412,"outFeatures":95}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":95,"outFeatures":653}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":653,"outFeatures":662}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":662,"outFeatures":188}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1180057}} {"id":"gen-ae-2231","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 555-dim input: encode down to a 236-dim bottleneck and decode back to 555. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2231\nComponents:\n - input (input) params={\"shape\":[1,555]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,555]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":555,"outFeatures":514}},{"componentType":"linear","name":"enc2","params":{"inFeatures":514,"outFeatures":236}},{"componentType":"linear","name":"dec1","params":{"inFeatures":236,"outFeatures":514}},{"componentType":"linear","name":"dec2","params":{"inFeatures":514,"outFeatures":555}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":813148}} {"id":"gen-cnn-2232","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 88 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2232\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":29211}} {"id":"gen-txf-2233","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 64-token sequences with 100 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2233\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,64]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8676,"embeddingDim":136}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":136,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":136,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1341504}} {"id":"gen-gqa-2234","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 239-token sequences with 79 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2234\nComponents:\n - input (input) params={\"shape\":[1,239]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,239]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":23884,"embeddingDim":624}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[624]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":624,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[624]}},{"componentType":"linear","name":"head","params":{"inFeatures":624,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":14952912}} {"id":"gen-fix-2235","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (146) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2235\nComponents:\n - input (input) params={\"shape\":[1,323]}\n - embed (embedding) params={\"numEmbeddings\":31746,\"embeddingDim\":146}\n - attn (multiHeadAttention) params={\"embedDim\":146,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":146,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4723976}} {"id":"gen-trim-2236","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3330-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2236\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - fc1 (linear) params={\"inFeatures\":116,\"outFeatures\":3330}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3330,\"outFeatures\":3330}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3330,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":116,"outFeatures":98}},{"type":"update_params","name":"fc2","params":{"inFeatures":98,"outFeatures":98}},{"type":"update_params","name":"head","params":{"inFeatures":98,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":21952}} {"id":"gen-norm-2237","family":"norm","seed":20260716,"spec":"This 132-feature, 57-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2237\nComponents:\n - input (input) params={\"shape\":[1,132]}\n - fc1 (linear) params={\"inFeatures\":132,\"outFeatures\":114}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":114,\"outFeatures\":469}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":469,\"outFeatures\":509}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":509,\"outFeatures\":57}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":114}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":469}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":509}}],"grade":{"pass":true,"score":100,"params":336248}} {"id":"gen-tower-2238","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 203-feature user input and a 197-feature item input, each through its own 2-layer MLP tower ending at width 427, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2238\nComponents:\n - user_input (input) params={\"shape\":[1,203]}\n - item_input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,203]}},{"componentType":"input","name":"item_input","params":{"shape":[1,197]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":203,"outFeatures":427}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":427,"outFeatures":427}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":197,"outFeatures":427}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":427,"outFeatures":427}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":854,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":536312}} {"id":"gen-grow-2239","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (79-feature input, 23 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2239\nComponents:\n - input (input) params={\"shape\":[1,79]}\n - fc1 (linear) params={\"inFeatures\":79,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":79,"outFeatures":1338}},{"type":"update_params","name":"fc2","params":{"inFeatures":1338,"outFeatures":1338}},{"type":"update_params","name":"head","params":{"inFeatures":1338,"outFeatures":23}}],"grade":{"pass":true,"score":80,"params":1926720}} {"id":"gen-mlp-2240","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 86-feature input with 145 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2240\nComponents:\n - input (input) params={\"shape\":[1,86]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,86]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":86,"outFeatures":682}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":682,"outFeatures":306}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":306,"outFeatures":580}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":580,"outFeatures":772}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":772,"outFeatures":501}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":501,"outFeatures":175}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":175,"outFeatures":145}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1392406}} {"id":"gen-ae-2241","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1063-dim input: encode down to a 134-dim bottleneck and decode back to 1063. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2241\nComponents:\n - input (input) params={\"shape\":[1,1063]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1063]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1063,"outFeatures":456}},{"componentType":"linear","name":"enc2","params":{"inFeatures":456,"outFeatures":134}},{"componentType":"linear","name":"dec1","params":{"inFeatures":134,"outFeatures":456}},{"componentType":"linear","name":"dec2","params":{"inFeatures":456,"outFeatures":1063}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1091664}} {"id":"gen-cnn-2242","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 86 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2242\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":43,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":29780}} {"id":"gen-txf-2243","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 272-token sequences with 69 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2243\nComponents:\n - input (input) params={\"shape\":[1,272]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,272]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22543,"embeddingDim":60}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":60,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1371120}} {"id":"gen-gqa-2244","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 138-token sequences with 15 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2244\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,138]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49428,"embeddingDim":72}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":72,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[72]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":72,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[72]}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3559896}} {"id":"gen-fix-2245","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (94) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2245\nComponents:\n - input (input) params={\"shape\":[1,328]}\n - embed (embedding) params={\"numEmbeddings\":19735,\"embeddingDim\":94}\n - attn (multiHeadAttention) params={\"embedDim\":94,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":94,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":1893348}} {"id":"gen-trim-2246","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2404-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2246\nComponents:\n - input (input) params={\"shape\":[1,171]}\n - fc1 (linear) params={\"inFeatures\":171,\"outFeatures\":2404}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2404,\"outFeatures\":2404}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2404,\"outFeatures\":2404}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2404,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":171,"outFeatures":372}},{"type":"update_params","name":"fc2","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"fc3","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"head","params":{"inFeatures":372,"outFeatures":49}}],"grade":{"pass":true,"score":84,"params":358608}} {"id":"gen-norm-2247","family":"norm","seed":20260716,"spec":"This 56-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2247\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - fc1 (linear) params={\"inFeatures\":56,\"outFeatures\":244}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":244,\"outFeatures\":441}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":441,\"outFeatures\":91}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":91,\"outFeatures\":439}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":439,\"outFeatures\":496}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":496,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":244}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":441}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":91}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":439}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":496}}],"grade":{"pass":true,"score":100,"params":445380}} {"id":"gen-tower-2248","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 158-feature user input and a 108-feature item input, each through its own 2-layer MLP tower ending at width 434, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2248\nComponents:\n - user_input (input) params={\"shape\":[1,158]}\n - item_input (input) params={\"shape\":[1,108]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,158]}},{"componentType":"input","name":"item_input","params":{"shape":[1,108]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":158,"outFeatures":434}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":434,"outFeatures":434}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":108,"outFeatures":434}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":434,"outFeatures":434}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":868,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":493024}} {"id":"gen-grow-2249","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (109-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2249\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - fc1 (linear) params={\"inFeatures\":109,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":109,"outFeatures":754}},{"type":"update_params","name":"fc2","params":{"inFeatures":754,"outFeatures":754}},{"type":"update_params","name":"head","params":{"inFeatures":754,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":678600}} {"id":"gen-mlp-2250","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 334-feature input with 108 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2250\nComponents:\n - input (input) params={\"shape\":[1,334]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,334]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":334,"outFeatures":438}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":438,"outFeatures":971}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":971,"outFeatures":131}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":131,"outFeatures":890}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":890,"outFeatures":67}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":67,"outFeatures":559}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":559,"outFeatures":108}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":972836}} {"id":"gen-ae-2251","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1055-dim input: encode down to a 118-dim bottleneck and decode back to 1055. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2251\nComponents:\n - input (input) params={\"shape\":[1,1055]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1055]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1055,"outFeatures":759}},{"componentType":"linear","name":"enc2","params":{"inFeatures":759,"outFeatures":118}},{"componentType":"linear","name":"dec1","params":{"inFeatures":118,"outFeatures":759}},{"componentType":"linear","name":"dec2","params":{"inFeatures":759,"outFeatures":1055}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1780614}} {"id":"gen-cnn-2252","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 94 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2252\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":42,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":29879}} {"id":"gen-txf-2253","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 131-token sequences with 3 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2253\nComponents:\n - input (input) params={\"shape\":[1,131]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,131]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45439,"embeddingDim":116}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":116,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":116,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":116,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5378920}} {"id":"gen-gqa-2254","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 371-token sequences with 8 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2254\nComponents:\n - input (input) params={\"shape\":[1,371]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,371]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9775,"embeddingDim":720}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":720,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[720]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":720,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[720]}},{"componentType":"linear","name":"head","params":{"inFeatures":720,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7043760}} {"id":"gen-trim-2256","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3245-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2256\nComponents:\n - input (input) params={\"shape\":[1,44]}\n - fc1 (linear) params={\"inFeatures\":44,\"outFeatures\":3245}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3245,\"outFeatures\":3245}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3245,\"outFeatures\":3245}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3245,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":44,"outFeatures":82}},{"type":"update_params","name":"fc2","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"fc3","params":{"inFeatures":82,"outFeatures":82}},{"type":"update_params","name":"head","params":{"inFeatures":82,"outFeatures":2}}],"grade":{"pass":true,"score":84,"params":17220}} {"id":"gen-norm-2257","family":"norm","seed":20260716,"spec":"This 90-feature, 61-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2257\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":37}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":37,\"outFeatures\":415}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":415,\"outFeatures\":451}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":451,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":37}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":415}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":451}}],"grade":{"pass":true,"score":100,"params":233361}} {"id":"gen-tower-2258","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 232-feature user input and a 247-feature item input, each through its own 2-layer MLP tower ending at width 325, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2258\nComponents:\n - user_input (input) params={\"shape\":[1,232]}\n - item_input (input) params={\"shape\":[1,247]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,232]}},{"componentType":"input","name":"item_input","params":{"shape":[1,247]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":232,"outFeatures":325}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":247,"outFeatures":325}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":325,"outFeatures":325}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":650,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":367575}} {"id":"gen-grow-2259","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (117-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2259\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":841}},{"type":"update_params","name":"fc2","params":{"inFeatures":841,"outFeatures":841}},{"type":"update_params","name":"head","params":{"inFeatures":841,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":814929}} {"id":"gen-mlp-2260","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 242-feature input with 67 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2260\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,242]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":242,"outFeatures":608}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":608,"outFeatures":569}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":569,"outFeatures":970}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":970,"outFeatures":701}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":701,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1771955}} {"id":"gen-ae-2261","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1779-dim input: encode down to a 118-dim bottleneck and decode back to 1779. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2261\nComponents:\n - input (input) params={\"shape\":[1,1779]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1779]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1779,"outFeatures":791}},{"componentType":"linear","name":"enc2","params":{"inFeatures":791,"outFeatures":118}},{"componentType":"linear","name":"dec1","params":{"inFeatures":118,"outFeatures":791}},{"componentType":"linear","name":"dec2","params":{"inFeatures":791,"outFeatures":1779}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3001054}} {"id":"gen-cnn-2262","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 57 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2262\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":14,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":10593}} {"id":"gen-txf-2263","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 312-token sequences with 48 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2263\nComponents:\n - input (input) params={\"shape\":[1,312]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,312]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29301,"embeddingDim":456}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":456,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":456,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":15878376}} {"id":"gen-gqa-2264","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 140-token sequences with 13 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2264\nComponents:\n - input (input) params={\"shape\":[1,140]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,140]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45209,"embeddingDim":656}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":656,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":656,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[656]}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":29665632}} {"id":"gen-fix-2265","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (250) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2265\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - embed (embedding) params={\"numEmbeddings\":32596,\"embeddingDim\":250}\n - attn (multiHeadAttention) params={\"embedDim\":250,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":250,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":8420750}} {"id":"gen-trim-2266","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3745-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2266\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":3745}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3745,\"outFeatures\":3745}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3745,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":217,"outFeatures":137}},{"type":"update_params","name":"fc2","params":{"inFeatures":137,"outFeatures":137}},{"type":"update_params","name":"head","params":{"inFeatures":137,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":52882}} {"id":"gen-norm-2267","family":"norm","seed":20260716,"spec":"This 228-feature, 56-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2267\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - fc1 (linear) params={\"inFeatures\":228,\"outFeatures\":112}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":112,\"outFeatures\":482}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":482,\"outFeatures\":90}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":90,\"outFeatures\":103}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":103,\"outFeatures\":56}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":112}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":482}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":90}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":103}}],"grade":{"pass":true,"score":100,"params":137938}} {"id":"gen-tower-2268","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 233-feature user input and a 189-feature item input, each through its own 2-layer MLP tower ending at width 224, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2268\nComponents:\n - user_input (input) params={\"shape\":[1,233]}\n - item_input (input) params={\"shape\":[1,189]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,233]}},{"componentType":"input","name":"item_input","params":{"shape":[1,189]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":233,"outFeatures":224}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":224,"outFeatures":224}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":189,"outFeatures":224}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":224,"outFeatures":224}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":448,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":195328}} {"id":"gen-grow-2269","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (121-feature input, 25 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2269\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":25}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":121,"outFeatures":908}},{"type":"update_params","name":"fc2","params":{"inFeatures":908,"outFeatures":908}},{"type":"update_params","name":"head","params":{"inFeatures":908,"outFeatures":25}}],"grade":{"pass":true,"score":80,"params":957032}} {"id":"gen-mlp-2270","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 345-feature input with 124 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2270\nComponents:\n - input (input) params={\"shape\":[1,345]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,345]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":345,"outFeatures":926}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":926,"outFeatures":951}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":951,"outFeatures":357}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":357,"outFeatures":124}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1583871}} {"id":"gen-ae-2271","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1899-dim input: encode down to a 145-dim bottleneck and decode back to 1899. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2271\nComponents:\n - input (input) params={\"shape\":[1,1899]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1899]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1899,"outFeatures":440}},{"componentType":"linear","name":"enc2","params":{"inFeatures":440,"outFeatures":145}},{"componentType":"linear","name":"dec1","params":{"inFeatures":145,"outFeatures":440}},{"componentType":"linear","name":"dec2","params":{"inFeatures":440,"outFeatures":1899}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1798720}} {"id":"gen-cnn-2272","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 97 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2272\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":29,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":12397}} {"id":"gen-txf-2273","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 45-token sequences with 65 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2273\nComponents:\n - input (input) params={\"shape\":[1,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,45]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21833,"embeddingDim":392}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":392,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":392,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":392,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":392,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":10427984}} {"id":"gen-gqa-2274","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 512-token sequences with 96 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2274\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8170,"embeddingDim":188}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":188,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[188]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":188,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[188]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":188,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[188]}},{"componentType":"linear","name":"head","params":{"inFeatures":188,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1554008}} {"id":"gen-fix-2275","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (70) is not divisible by numHeads (11). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2275\nComponents:\n - input (input) params={\"shape\":[1,310]}\n - embed (embedding) params={\"numEmbeddings\":45665,\"embeddingDim\":70}\n - attn (multiHeadAttention) params={\"embedDim\":70,\"numHeads\":11}\n - head (linear) params={\"inFeatures\":70,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":3221260}} {"id":"gen-trim-2276","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3388-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2276\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - fc1 (linear) params={\"inFeatures\":183,\"outFeatures\":3388}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3388,\"outFeatures\":3388}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3388,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":183,"outFeatures":259}},{"type":"update_params","name":"fc2","params":{"inFeatures":259,"outFeatures":259}},{"type":"update_params","name":"head","params":{"inFeatures":259,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":115514}} {"id":"gen-norm-2277","family":"norm","seed":20260716,"spec":"This 164-feature, 62-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2277\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - fc1 (linear) params={\"inFeatures\":164,\"outFeatures\":238}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":238,\"outFeatures\":341}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":341,\"outFeatures\":34}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":34,\"outFeatures\":106}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":106,\"outFeatures\":273}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":273,\"outFeatures\":62}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":238}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":341}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":34}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":106}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":273}}],"grade":{"pass":true,"score":100,"params":181252}} {"id":"gen-tower-2278","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 93-feature user input and a 103-feature item input, each through its own 2-layer MLP tower ending at width 429, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2278\nComponents:\n - user_input (input) params={\"shape\":[1,93]}\n - item_input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,93]}},{"componentType":"input","name":"item_input","params":{"shape":[1,103]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":93,"outFeatures":429}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":429,"outFeatures":429}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":103,"outFeatures":429}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":429,"outFeatures":429}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":858,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":453024}} {"id":"gen-grow-2279","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (56-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2279\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - fc1 (linear) params={\"inFeatures\":56,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":56,"outFeatures":686}},{"type":"update_params","name":"fc2","params":{"inFeatures":686,"outFeatures":686}},{"type":"update_params","name":"head","params":{"inFeatures":686,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":531650}} {"id":"gen-mlp-2280","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 297-feature input with 33 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2280\nComponents:\n - input (input) params={\"shape\":[1,297]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,297]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":297,"outFeatures":340}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":340,"outFeatures":616}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":616,"outFeatures":938}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":938,"outFeatures":414}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":414,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1290222}} {"id":"gen-ae-2281","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 848-dim input: encode down to a 95-dim bottleneck and decode back to 848. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2281\nComponents:\n - input (input) params={\"shape\":[1,848]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,848]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":848,"outFeatures":204}},{"componentType":"linear","name":"enc2","params":{"inFeatures":204,"outFeatures":95}},{"componentType":"linear","name":"dec1","params":{"inFeatures":95,"outFeatures":204}},{"componentType":"linear","name":"dec2","params":{"inFeatures":204,"outFeatures":848}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":384744}} {"id":"gen-cnn-2282","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 8 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2282\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":38,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":39602}} {"id":"gen-txf-2283","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 340-token sequences with 78 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2283\nComponents:\n - input (input) params={\"shape\":[1,340]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,340]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26440,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":176,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":176,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5038880}} {"id":"gen-gqa-2284","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 484-token sequences with 17 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2284\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12742,"embeddingDim":152}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":152,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[152]}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1939368}} {"id":"gen-fix-2285","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (346) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2285\nComponents:\n - input (input) params={\"shape\":[1,229]}\n - embed (embedding) params={\"numEmbeddings\":12427,\"embeddingDim\":346}\n - attn (multiHeadAttention) params={\"embedDim\":346,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":346,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4802826}} {"id":"gen-trim-2286","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3352-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2286\nComponents:\n - input (input) params={\"shape\":[1,140]}\n - fc1 (linear) params={\"inFeatures\":140,\"outFeatures\":3352}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3352,\"outFeatures\":3352}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3352,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":140,"outFeatures":68}},{"type":"update_params","name":"fc2","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"head","params":{"inFeatures":68,"outFeatures":9}}],"grade":{"pass":true,"score":80,"params":14756}} {"id":"gen-norm-2287","family":"norm","seed":20260716,"spec":"This 206-feature, 61-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2287\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":246}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":246,\"outFeatures\":444}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":444,\"outFeatures\":284}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":284,\"outFeatures\":61}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":246}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":444}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":284}}],"grade":{"pass":true,"score":100,"params":303320}} {"id":"gen-tower-2288","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 246-feature user input and a 25-feature item input, each through its own 2-layer MLP tower ending at width 113, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2288\nComponents:\n - user_input (input) params={\"shape\":[1,246]}\n - item_input (input) params={\"shape\":[1,25]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,246]}},{"componentType":"input","name":"item_input","params":{"shape":[1,25]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":246,"outFeatures":113}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":113,"outFeatures":113}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":25,"outFeatures":113}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":113,"outFeatures":113}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":226,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":56387}} {"id":"gen-grow-2289","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (37-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2289\nComponents:\n - input (input) params={\"shape\":[1,37]}\n - fc1 (linear) params={\"inFeatures\":37,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":37,"outFeatures":922}},{"type":"update_params","name":"fc2","params":{"inFeatures":922,"outFeatures":922}},{"type":"update_params","name":"head","params":{"inFeatures":922,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":929376}} {"id":"gen-mlp-2290","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 342-feature input with 92 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2290\nComponents:\n - input (input) params={\"shape\":[1,342]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,342]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":342,"outFeatures":832}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":832,"outFeatures":669}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":669,"outFeatures":69}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":69,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":893661}} {"id":"gen-ae-2291","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 993-dim input: encode down to a 104-dim bottleneck and decode back to 993. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2291\nComponents:\n - input (input) params={\"shape\":[1,993]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,993]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":993,"outFeatures":295}},{"componentType":"linear","name":"enc2","params":{"inFeatures":295,"outFeatures":104}},{"componentType":"linear","name":"dec1","params":{"inFeatures":104,"outFeatures":295}},{"componentType":"linear","name":"dec2","params":{"inFeatures":295,"outFeatures":993}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":647230}} {"id":"gen-cnn-2292","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x28x28 image with 93 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2292\nComponents:\n - input (input) params={\"shape\":[1,3,28,28]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,28,28]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":25,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":11544}} {"id":"gen-txf-2293","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 443-token sequences with 70 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2293\nComponents:\n - input (input) params={\"shape\":[1,443]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,443]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11639,"embeddingDim":106}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":106,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":106,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":106,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":106,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1375986}} {"id":"gen-gqa-2294","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 365-token sequences with 29 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2294\nComponents:\n - input (input) params={\"shape\":[1,365]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,365]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9596,"embeddingDim":360}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":360,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[360]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":360,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[360]}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3465000}} {"id":"gen-trim-2296","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2028-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2296\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":2028}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2028,\"outFeatures\":2028}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2028,\"outFeatures\":2028}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2028,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":190,"outFeatures":314}},{"type":"update_params","name":"fc2","params":{"inFeatures":314,"outFeatures":314}},{"type":"update_params","name":"fc3","params":{"inFeatures":314,"outFeatures":314}},{"type":"update_params","name":"head","params":{"inFeatures":314,"outFeatures":18}}],"grade":{"pass":true,"score":84,"params":262504}} {"id":"gen-norm-2297","family":"norm","seed":20260716,"spec":"This 151-feature, 69-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2297\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - fc1 (linear) params={\"inFeatures\":151,\"outFeatures\":318}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":318,\"outFeatures\":93}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":93,\"outFeatures\":324}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":324,\"outFeatures\":292}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":292,\"outFeatures\":69}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":318}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":93}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":324}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":292}}],"grade":{"pass":true,"score":100,"params":222480}} {"id":"gen-tower-2298","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 125-feature user input and a 158-feature item input, each through its own 2-layer MLP tower ending at width 63, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2298\nComponents:\n - user_input (input) params={\"shape\":[1,125]}\n - item_input (input) params={\"shape\":[1,158]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,125]}},{"componentType":"input","name":"item_input","params":{"shape":[1,158]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":125,"outFeatures":63}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":63,"outFeatures":63}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":158,"outFeatures":63}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":63,"outFeatures":63}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":126,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":25893}} {"id":"gen-grow-2299","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (64-feature input, 12 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2299\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":64,"outFeatures":1340}},{"type":"update_params","name":"fc2","params":{"inFeatures":1340,"outFeatures":1340}},{"type":"update_params","name":"head","params":{"inFeatures":1340,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":1897440}} {"id":"gen-mlp-2300","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 396-feature input with 178 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2300\nComponents:\n - input (input) params={\"shape\":[1,396]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,396]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":396,"outFeatures":104}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":104,"outFeatures":238}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":238,"outFeatures":130}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":130,"outFeatures":314}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":314,"outFeatures":115}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":115,"outFeatures":258}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":258,"outFeatures":178}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":249400}} {"id":"gen-ae-2301","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 597-dim input: encode down to a 144-dim bottleneck and decode back to 597. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2301\nComponents:\n - input (input) params={\"shape\":[1,597]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,597]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":597,"outFeatures":155}},{"componentType":"linear","name":"enc2","params":{"inFeatures":155,"outFeatures":144}},{"componentType":"linear","name":"dec1","params":{"inFeatures":144,"outFeatures":155}},{"componentType":"linear","name":"dec2","params":{"inFeatures":155,"outFeatures":597}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":229710}} {"id":"gen-cnn-2302","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 68 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2302\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":31,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":46,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":33,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":48868}} {"id":"gen-txf-2303","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 350-token sequences with 98 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2303\nComponents:\n - input (input) params={\"shape\":[1,350]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,350]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22903,"embeddingDim":488}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":488,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":488,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":14082216}} {"id":"gen-gqa-2304","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 39-token sequences with 27 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2304\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,39]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17702,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3120304}} {"id":"gen-trim-2306","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3353-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2306\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - fc1 (linear) params={\"inFeatures\":209,\"outFeatures\":3353}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3353,\"outFeatures\":3353}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3353,\"outFeatures\":3353}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3353,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":209,"outFeatures":274}},{"type":"update_params","name":"fc2","params":{"inFeatures":274,"outFeatures":274}},{"type":"update_params","name":"fc3","params":{"inFeatures":274,"outFeatures":274}},{"type":"update_params","name":"head","params":{"inFeatures":274,"outFeatures":41}}],"grade":{"pass":true,"score":84,"params":218652}} {"id":"gen-norm-2307","family":"norm","seed":20260716,"spec":"This 173-feature, 54-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2307\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - fc1 (linear) params={\"inFeatures\":173,\"outFeatures\":64}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":64,\"outFeatures\":511}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":511,\"outFeatures\":281}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":281,\"outFeatures\":69}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":69,\"outFeatures\":83}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":83,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":64}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":511}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":281}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":69}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":83}}],"grade":{"pass":true,"score":100,"params":216965}} {"id":"gen-tower-2308","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 166-feature user input and a 170-feature item input, each through its own 2-layer MLP tower ending at width 343, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2308\nComponents:\n - user_input (input) params={\"shape\":[1,166]}\n - item_input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,166]}},{"componentType":"input","name":"item_input","params":{"shape":[1,170]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":166,"outFeatures":343}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":343,"outFeatures":343}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":170,"outFeatures":343}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":343,"outFeatures":343}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":686,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":351232}} {"id":"gen-grow-2309","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (128-feature input, 6 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2309\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":128,"outFeatures":1154}},{"type":"update_params","name":"fc2","params":{"inFeatures":1154,"outFeatures":1154}},{"type":"update_params","name":"head","params":{"inFeatures":1154,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":1486352}} {"id":"gen-mlp-2310","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 110-feature input with 168 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2310\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,110]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":110,"outFeatures":897}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":897,"outFeatures":537}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":537,"outFeatures":749}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":749,"outFeatures":757}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":757,"outFeatures":652}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":652,"outFeatures":743}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":743,"outFeatures":168}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2652389}} {"id":"gen-ae-2311","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 922-dim input: encode down to a 132-dim bottleneck and decode back to 922. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2311\nComponents:\n - input (input) params={\"shape\":[1,922]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,922]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":922,"outFeatures":922}},{"componentType":"linear","name":"enc2","params":{"inFeatures":922,"outFeatures":132}},{"componentType":"linear","name":"dec1","params":{"inFeatures":132,"outFeatures":922}},{"componentType":"linear","name":"dec2","params":{"inFeatures":922,"outFeatures":922}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1943576}} {"id":"gen-cnn-2312","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 70 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2312\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":52,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":34,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":62889}} {"id":"gen-txf-2313","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 382-token sequences with 75 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2313\nComponents:\n - input (input) params={\"shape\":[1,382]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,382]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8936,"embeddingDim":118}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":118,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":118,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1118994}} {"id":"gen-gqa-2314","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 173-token sequences with 51 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2314\nComponents:\n - input (input) params={\"shape\":[1,173]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,173]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26777,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":272,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":272,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7297216}} {"id":"gen-fix-2315","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (184) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2315\nComponents:\n - input (input) params={\"shape\":[1,501]}\n - embed (embedding) params={\"numEmbeddings\":28750,\"embeddingDim\":184}\n - attn (multiHeadAttention) params={\"embedDim\":184,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":184,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":5425792}} {"id":"gen-trim-2316","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3499-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2316\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - fc1 (linear) params={\"inFeatures\":183,\"outFeatures\":3499}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3499,\"outFeatures\":3499}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3499,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":183,"outFeatures":342}},{"type":"update_params","name":"fc2","params":{"inFeatures":342,"outFeatures":342}},{"type":"update_params","name":"head","params":{"inFeatures":342,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":190152}} {"id":"gen-norm-2317","family":"norm","seed":20260716,"spec":"This 193-feature, 29-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2317\nComponents:\n - input (input) params={\"shape\":[1,193]}\n - fc1 (linear) params={\"inFeatures\":193,\"outFeatures\":411}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":411,\"outFeatures\":197}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":197,\"outFeatures\":241}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":241,\"outFeatures\":356}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":356,\"outFeatures\":353}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":353,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":411}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":197}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":241}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":356}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":353}}],"grade":{"pass":true,"score":100,"params":429468}} {"id":"gen-tower-2318","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 204-feature user input and a 250-feature item input, each through its own 2-layer MLP tower ending at width 82, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2318\nComponents:\n - user_input (input) params={\"shape\":[1,204]}\n - item_input (input) params={\"shape\":[1,250]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,204]}},{"componentType":"input","name":"item_input","params":{"shape":[1,250]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":204,"outFeatures":82}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":250,"outFeatures":82}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":82,"outFeatures":82}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":164,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":50840}} {"id":"gen-grow-2319","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (90-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2319\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":90,"outFeatures":813}},{"type":"update_params","name":"fc2","params":{"inFeatures":813,"outFeatures":813}},{"type":"update_params","name":"head","params":{"inFeatures":813,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":769098}} {"id":"gen-mlp-2320","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 267-feature input with 128 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2320\nComponents:\n - input (input) params={\"shape\":[1,267]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,267]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":267,"outFeatures":813}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":813,"outFeatures":1010}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1010,"outFeatures":128}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1167481}} {"id":"gen-ae-2321","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1740-dim input: encode down to a 158-dim bottleneck and decode back to 1740. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2321\nComponents:\n - input (input) params={\"shape\":[1,1740]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1740]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1740,"outFeatures":1012}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1012,"outFeatures":158}},{"componentType":"linear","name":"dec1","params":{"inFeatures":158,"outFeatures":1012}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1012,"outFeatures":1740}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3841552}} {"id":"gen-cnn-2322","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 44 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2322\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":40,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":19,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":8,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":16132}} {"id":"gen-txf-2323","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 139-token sequences with 76 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2323\nComponents:\n - input (input) params={\"shape\":[1,139]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,139]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31751,"embeddingDim":456}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":456,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":456,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":15344856}} {"id":"gen-gqa-2324","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 240-token sequences with 59 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2324\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,240]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17238,"embeddingDim":60}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":60,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[60]}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1037820}} {"id":"gen-trim-2326","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2247-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2326\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - fc1 (linear) params={\"inFeatures\":156,\"outFeatures\":2247}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2247,\"outFeatures\":2247}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2247,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":156,"outFeatures":171}},{"type":"update_params","name":"fc2","params":{"inFeatures":171,"outFeatures":171}},{"type":"update_params","name":"head","params":{"inFeatures":171,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":59679}} {"id":"gen-norm-2327","family":"norm","seed":20260716,"spec":"This 118-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2327\nComponents:\n - input (input) params={\"shape\":[1,118]}\n - fc1 (linear) params={\"inFeatures\":118,\"outFeatures\":435}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":435,\"outFeatures\":129}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":129,\"outFeatures\":491}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":491,\"outFeatures\":46}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":46,\"outFeatures\":394}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":394,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":435}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":129}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":491}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":46}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":394}}],"grade":{"pass":true,"score":100,"params":235134}} {"id":"gen-tower-2328","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 142-feature user input and a 238-feature item input, each through its own 2-layer MLP tower ending at width 425, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2328\nComponents:\n - user_input (input) params={\"shape\":[1,142]}\n - item_input (input) params={\"shape\":[1,238]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,142]}},{"componentType":"input","name":"item_input","params":{"shape":[1,238]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":142,"outFeatures":425}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":238,"outFeatures":425}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":425,"outFeatures":425}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":850,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":523600}} {"id":"gen-grow-2329","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (70-feature input, 50 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2329\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":70,"outFeatures":734}},{"type":"update_params","name":"fc2","params":{"inFeatures":734,"outFeatures":734}},{"type":"update_params","name":"head","params":{"inFeatures":734,"outFeatures":50}}],"grade":{"pass":true,"score":80,"params":626836}} {"id":"gen-mlp-2330","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 12-feature input with 184 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2330\nComponents:\n - input (input) params={\"shape\":[1,12]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,12]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":12,"outFeatures":755}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":755,"outFeatures":845}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":845,"outFeatures":559}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":559,"outFeatures":184}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1222246}} {"id":"gen-ae-2331","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1978-dim input: encode down to a 17-dim bottleneck and decode back to 1978. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2331\nComponents:\n - input (input) params={\"shape\":[1,1978]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1978]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1978,"outFeatures":930}},{"componentType":"linear","name":"enc2","params":{"inFeatures":930,"outFeatures":17}},{"componentType":"linear","name":"dec1","params":{"inFeatures":17,"outFeatures":930}},{"componentType":"linear","name":"dec2","params":{"inFeatures":930,"outFeatures":1978}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3710700}} {"id":"gen-cnn-2332","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x51x51 image with 22 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2332\nComponents:\n - input (input) params={\"shape\":[1,3,51,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,51,51]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":36,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":8,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":36,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":28300}} {"id":"gen-txf-2333","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 119-token sequences with 50 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2333\nComponents:\n - input (input) params={\"shape\":[1,119]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,119]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45544,"embeddingDim":400}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":400,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":18877600}} {"id":"gen-gqa-2334","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 455-token sequences with 31 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2334\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,455]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38995,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":80,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3122080}} {"id":"gen-trim-2336","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1841-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2336\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":1841}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1841,\"outFeatures\":1841}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1841,\"outFeatures\":1841}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1841,\"outFeatures\":1841}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1841,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":128,"outFeatures":345}},{"type":"update_params","name":"fc2","params":{"inFeatures":345,"outFeatures":345}},{"type":"update_params","name":"fc3","params":{"inFeatures":345,"outFeatures":345}},{"type":"update_params","name":"fc4","params":{"inFeatures":345,"outFeatures":345}},{"type":"update_params","name":"head","params":{"inFeatures":345,"outFeatures":42}}],"grade":{"pass":true,"score":88,"params":415725}} {"id":"gen-norm-2337","family":"norm","seed":20260716,"spec":"This 49-feature, 37-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2337\nComponents:\n - input (input) params={\"shape\":[1,49]}\n - fc1 (linear) params={\"inFeatures\":49,\"outFeatures\":392}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":392,\"outFeatures\":410}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":410,\"outFeatures\":286}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":286,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":392}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":410}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":286}}],"grade":{"pass":true,"score":100,"params":307770}} {"id":"gen-tower-2338","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 208-feature user input and a 239-feature item input, each through its own 2-layer MLP tower ending at width 178, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2338\nComponents:\n - user_input (input) params={\"shape\":[1,208]}\n - item_input (input) params={\"shape\":[1,239]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,208]}},{"componentType":"input","name":"item_input","params":{"shape":[1,239]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":208,"outFeatures":178}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":178,"outFeatures":178}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":239,"outFeatures":178}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":178,"outFeatures":178}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":356,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":143290}} {"id":"gen-grow-2339","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (17-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2339\nComponents:\n - input (input) params={\"shape\":[1,17]}\n - fc1 (linear) params={\"inFeatures\":17,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":17,"outFeatures":1159}},{"type":"update_params","name":"fc2","params":{"inFeatures":1159,"outFeatures":1159}},{"type":"update_params","name":"head","params":{"inFeatures":1159,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":1371097}} {"id":"gen-mlp-2340","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 433-feature input with 83 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2340\nComponents:\n - input (input) params={\"shape\":[1,433]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,433]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":433,"outFeatures":543}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":543,"outFeatures":603}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":603,"outFeatures":157}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":157,"outFeatures":715}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":715,"outFeatures":88}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":88,"outFeatures":352}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":352,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":892586}} {"id":"gen-ae-2341","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 196-dim input: encode down to a 34-dim bottleneck and decode back to 196. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2341\nComponents:\n - input (input) params={\"shape\":[1,196]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,196]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":196,"outFeatures":275}},{"componentType":"linear","name":"enc2","params":{"inFeatures":275,"outFeatures":34}},{"componentType":"linear","name":"dec1","params":{"inFeatures":34,"outFeatures":275}},{"componentType":"linear","name":"dec2","params":{"inFeatures":275,"outFeatures":196}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":126500}} {"id":"gen-cnn-2342","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 58 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2342\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":39,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":63,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":55852}} {"id":"gen-txf-2343","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 212-token sequences with 32 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2343\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,212]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13735,"embeddingDim":200}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":200,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":200,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3233400}} {"id":"gen-gqa-2344","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 379-token sequences with 86 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2344\nComponents:\n - input (input) params={\"shape\":[1,379]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,379]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17241,"embeddingDim":512}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":512,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[512]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":512,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[512]}},{"componentType":"linear","name":"head","params":{"inFeatures":512,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8871424}} {"id":"gen-fix-2345","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (88) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2345\nComponents:\n - input (input) params={\"shape\":[1,191]}\n - embed (embedding) params={\"numEmbeddings\":33065,\"embeddingDim\":88}\n - attn (multiHeadAttention) params={\"embedDim\":88,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":88,\"outFeatures\":64}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":2946328}} {"id":"gen-trim-2346","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1576-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2346\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":1576}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1576,\"outFeatures\":1576}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1576,\"outFeatures\":1576}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1576,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":231}},{"type":"update_params","name":"fc2","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"fc3","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"head","params":{"inFeatures":231,"outFeatures":39}}],"grade":{"pass":true,"score":84,"params":130746}} {"id":"gen-norm-2347","family":"norm","seed":20260716,"spec":"This 52-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2347\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - fc1 (linear) params={\"inFeatures\":52,\"outFeatures\":123}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":123,\"outFeatures\":364}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":364,\"outFeatures\":455}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":455,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":123}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":364}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":455}}],"grade":{"pass":true,"score":100,"params":231803}} {"id":"gen-tower-2348","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 163-feature user input and a 205-feature item input, each through its own 2-layer MLP tower ending at width 94, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2348\nComponents:\n - user_input (input) params={\"shape\":[1,163]}\n - item_input (input) params={\"shape\":[1,205]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,163]}},{"componentType":"input","name":"item_input","params":{"shape":[1,205]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":163,"outFeatures":94}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":205,"outFeatures":94}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":94,"outFeatures":94}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":188,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":52452}} {"id":"gen-grow-2349","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (55-feature input, 35 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2349\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - fc1 (linear) params={\"inFeatures\":55,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":35}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":55,"outFeatures":705}},{"type":"update_params","name":"fc2","params":{"inFeatures":705,"outFeatures":705}},{"type":"update_params","name":"head","params":{"inFeatures":705,"outFeatures":35}}],"grade":{"pass":true,"score":80,"params":560475}} {"id":"gen-mlp-2350","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 485-feature input with 167 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2350\nComponents:\n - input (input) params={\"shape\":[1,485]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,485]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":485,"outFeatures":232}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":232,"outFeatures":232}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":167}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":205088}} {"id":"gen-ae-2351","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1916-dim input: encode down to a 32-dim bottleneck and decode back to 1916. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2351\nComponents:\n - input (input) params={\"shape\":[1,1916]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1916]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1916,"outFeatures":458}},{"componentType":"linear","name":"enc2","params":{"inFeatures":458,"outFeatures":32}},{"componentType":"linear","name":"dec1","params":{"inFeatures":32,"outFeatures":458}},{"componentType":"linear","name":"dec2","params":{"inFeatures":458,"outFeatures":1916}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1784368}} {"id":"gen-cnn-2352","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 90 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2352\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":27,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":10053}} {"id":"gen-txf-2353","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 91-token sequences with 55 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2353\nComponents:\n - input (input) params={\"shape\":[1,91]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,91]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12081,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":80,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1047680}} {"id":"gen-gqa-2354","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 490-token sequences with 47 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2354\nComponents:\n - input (input) params={\"shape\":[1,490]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,490]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48282,"embeddingDim":592}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":592,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[592]}},{"componentType":"linear","name":"head","params":{"inFeatures":592,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":28610768}} {"id":"gen-fix-2355","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (248) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2355\nComponents:\n - input (input) params={\"shape\":[1,378]}\n - embed (embedding) params={\"numEmbeddings\":40211,\"embeddingDim\":248}\n - attn (multiHeadAttention) params={\"embedDim\":248,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":248,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":10237688}} {"id":"gen-trim-2356","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1991-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2356\nComponents:\n - input (input) params={\"shape\":[1,253]}\n - fc1 (linear) params={\"inFeatures\":253,\"outFeatures\":1991}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1991,\"outFeatures\":1991}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1991,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":253,"outFeatures":214}},{"type":"update_params","name":"fc2","params":{"inFeatures":214,"outFeatures":214}},{"type":"update_params","name":"head","params":{"inFeatures":214,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":107856}} {"id":"gen-norm-2357","family":"norm","seed":20260716,"spec":"This 190-feature, 39-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2357\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":304}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":304,\"outFeatures\":301}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":301,\"outFeatures\":409}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":409,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":304}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":301}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":409}}],"grade":{"pass":true,"score":100,"params":288324}} {"id":"gen-tower-2358","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 147-feature user input and a 154-feature item input, each through its own 2-layer MLP tower ending at width 177, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2358\nComponents:\n - user_input (input) params={\"shape\":[1,147]}\n - item_input (input) params={\"shape\":[1,154]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,147]}},{"componentType":"input","name":"item_input","params":{"shape":[1,154]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":147,"outFeatures":177}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":177,"outFeatures":177}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":154,"outFeatures":177}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":177,"outFeatures":177}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":354,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":116289}} {"id":"gen-grow-2359","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (63-feature input, 36 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2359\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - fc1 (linear) params={\"inFeatures\":63,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":63,"outFeatures":1343}},{"type":"update_params","name":"fc2","params":{"inFeatures":1343,"outFeatures":1343}},{"type":"update_params","name":"head","params":{"inFeatures":1343,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":1936606}} {"id":"gen-mlp-2360","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 141-feature input with 22 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2360\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,141]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":141,"outFeatures":680}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":680,"outFeatures":697}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":697,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":585174}} {"id":"gen-ae-2361","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 295-dim input: encode down to a 60-dim bottleneck and decode back to 295. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2361\nComponents:\n - input (input) params={\"shape\":[1,295]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,295]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":295,"outFeatures":339}},{"componentType":"linear","name":"enc2","params":{"inFeatures":339,"outFeatures":60}},{"componentType":"linear","name":"dec1","params":{"inFeatures":60,"outFeatures":339}},{"componentType":"linear","name":"dec2","params":{"inFeatures":339,"outFeatures":295}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":240690}} {"id":"gen-cnn-2362","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 25 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2362\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":21,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":53,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":25617}} {"id":"gen-txf-2363","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 244-token sequences with 62 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2363\nComponents:\n - input (input) params={\"shape\":[1,244]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,244]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44665,"embeddingDim":228}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":228,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":228,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":228,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":228,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":10821564}} {"id":"gen-gqa-2364","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 424-token sequences with 97 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2364\nComponents:\n - input (input) params={\"shape\":[1,424]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,424]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11096,"embeddingDim":480}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":480,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[480]}},{"componentType":"linear","name":"head","params":{"inFeatures":480,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5372640}} {"id":"gen-trim-2366","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1622-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2366\nComponents:\n - input (input) params={\"shape\":[1,184]}\n - fc1 (linear) params={\"inFeatures\":184,\"outFeatures\":1622}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1622,\"outFeatures\":1622}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1622,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":184,"outFeatures":96}},{"type":"update_params","name":"fc2","params":{"inFeatures":96,"outFeatures":96}},{"type":"update_params","name":"head","params":{"inFeatures":96,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":28800}} {"id":"gen-norm-2367","family":"norm","seed":20260716,"spec":"This 155-feature, 62-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2367\nComponents:\n - input (input) params={\"shape\":[1,155]}\n - fc1 (linear) params={\"inFeatures\":155,\"outFeatures\":91}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":91,\"outFeatures\":66}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":66,\"outFeatures\":357}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":357,\"outFeatures\":62}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":91}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":66}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":357}}],"grade":{"pass":true,"score":100,"params":65807}} {"id":"gen-tower-2368","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 252-feature user input and a 96-feature item input, each through its own 2-layer MLP tower ending at width 237, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2368\nComponents:\n - user_input (input) params={\"shape\":[1,252]}\n - item_input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,252]}},{"componentType":"input","name":"item_input","params":{"shape":[1,96]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":252,"outFeatures":237}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":237,"outFeatures":237}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":96,"outFeatures":237}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":237,"outFeatures":237}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":474,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":195288}} {"id":"gen-grow-2369","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (90-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2369\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - fc1 (linear) params={\"inFeatures\":90,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":90,"outFeatures":844}},{"type":"update_params","name":"fc2","params":{"inFeatures":844,"outFeatures":844}},{"type":"update_params","name":"head","params":{"inFeatures":844,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":829652}} {"id":"gen-mlp-2370","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 19-feature input with 171 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2370\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,19]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":19,"outFeatures":807}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":807,"outFeatures":261}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":261,"outFeatures":38}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":171}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":242376}} {"id":"gen-ae-2371","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1174-dim input: encode down to a 45-dim bottleneck and decode back to 1174. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2371\nComponents:\n - input (input) params={\"shape\":[1,1174]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1174]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1174,"outFeatures":486}},{"componentType":"linear","name":"enc2","params":{"inFeatures":486,"outFeatures":45}},{"componentType":"linear","name":"dec1","params":{"inFeatures":45,"outFeatures":486}},{"componentType":"linear","name":"dec2","params":{"inFeatures":486,"outFeatures":1174}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1184868}} {"id":"gen-cnn-2372","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 55 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2372\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":11,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":5684}} {"id":"gen-txf-2373","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 143-token sequences with 70 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2373\nComponents:\n - input (input) params={\"shape\":[1,143]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,143]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15698,"embeddingDim":244}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":244,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":244,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4085536}} {"id":"gen-gqa-2374","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 300-token sequences with 19 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2374\nComponents:\n - input (input) params={\"shape\":[1,300]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,300]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9549,"embeddingDim":112}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":112,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[112]}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1071616}} {"id":"gen-fix-2375","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (480) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2375\nComponents:\n - input (input) params={\"shape\":[1,150]}\n - embed (embedding) params={\"numEmbeddings\":37579,\"embeddingDim\":480}\n - attn (multiHeadAttention) params={\"embedDim\":480,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":480,\"outFeatures\":85}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":19000320}} {"id":"gen-trim-2376","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3081-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2376\nComponents:\n - input (input) params={\"shape\":[1,139]}\n - fc1 (linear) params={\"inFeatures\":139,\"outFeatures\":3081}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3081,\"outFeatures\":3081}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3081,\"outFeatures\":3081}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3081,\"outFeatures\":3081}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3081,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":139,"outFeatures":162}},{"type":"update_params","name":"fc2","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"fc3","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"fc4","params":{"inFeatures":162,"outFeatures":162}},{"type":"update_params","name":"head","params":{"inFeatures":162,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":104004}} {"id":"gen-norm-2377","family":"norm","seed":20260716,"spec":"This 158-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2377\nComponents:\n - input (input) params={\"shape\":[1,158]}\n - fc1 (linear) params={\"inFeatures\":158,\"outFeatures\":299}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":299,\"outFeatures\":244}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":244,\"outFeatures\":205}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":205,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":299}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":244}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":205}}],"grade":{"pass":true,"score":100,"params":185388}} {"id":"gen-tower-2378","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 79-feature user input and a 90-feature item input, each through its own 2-layer MLP tower ending at width 296, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2378\nComponents:\n - user_input (input) params={\"shape\":[1,79]}\n - item_input (input) params={\"shape\":[1,90]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,79]}},{"componentType":"input","name":"item_input","params":{"shape":[1,90]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":79,"outFeatures":296}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":296,"outFeatures":296}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":90,"outFeatures":296}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":296,"outFeatures":296}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":592,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":225848}} {"id":"gen-grow-2379","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (66-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2379\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":66,"outFeatures":687}},{"type":"update_params","name":"fc2","params":{"inFeatures":687,"outFeatures":687}},{"type":"update_params","name":"head","params":{"inFeatures":687,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":532425}} {"id":"gen-mlp-2380","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 238-feature input with 156 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2380\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,238]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":238,"outFeatures":390}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":390,"outFeatures":36}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":36,"outFeatures":913}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":913,"outFeatures":156}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":282156}} {"id":"gen-ae-2381","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 532-dim input: encode down to a 175-dim bottleneck and decode back to 532. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2381\nComponents:\n - input (input) params={\"shape\":[1,532]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,532]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":532,"outFeatures":314}},{"componentType":"linear","name":"enc2","params":{"inFeatures":314,"outFeatures":175}},{"componentType":"linear","name":"dec1","params":{"inFeatures":175,"outFeatures":314}},{"componentType":"linear","name":"dec2","params":{"inFeatures":314,"outFeatures":532}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":443996}} {"id":"gen-cnn-2382","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 98 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2382\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":49,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":20246}} {"id":"gen-txf-2383","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 462-token sequences with 39 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2383\nComponents:\n - input (input) params={\"shape\":[1,462]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,462]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43896,"embeddingDim":50}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":50,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":50,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2206750}} {"id":"gen-gqa-2384","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 309-token sequences with 41 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2384\nComponents:\n - input (input) params={\"shape\":[1,309]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,309]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48433,"embeddingDim":92}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[92]}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4459608}} {"id":"gen-trim-2386","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3448-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2386\nComponents:\n - input (input) params={\"shape\":[1,77]}\n - fc1 (linear) params={\"inFeatures\":77,\"outFeatures\":3448}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3448,\"outFeatures\":3448}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3448,\"outFeatures\":3448}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3448,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":77,"outFeatures":84}},{"type":"update_params","name":"fc2","params":{"inFeatures":84,"outFeatures":84}},{"type":"update_params","name":"fc3","params":{"inFeatures":84,"outFeatures":84}},{"type":"update_params","name":"head","params":{"inFeatures":84,"outFeatures":47}}],"grade":{"pass":true,"score":84,"params":24528}} {"id":"gen-norm-2387","family":"norm","seed":20260716,"spec":"This 194-feature, 90-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2387\nComponents:\n - input (input) params={\"shape\":[1,194]}\n - fc1 (linear) params={\"inFeatures\":194,\"outFeatures\":113}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":113,\"outFeatures\":232}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":232,\"outFeatures\":430}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":430,\"outFeatures\":90}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":113}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":232}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":430}}],"grade":{"pass":true,"score":100,"params":186598}} {"id":"gen-tower-2388","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 32-feature user input and a 78-feature item input, each through its own 2-layer MLP tower ending at width 88, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2388\nComponents:\n - user_input (input) params={\"shape\":[1,32]}\n - item_input (input) params={\"shape\":[1,78]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,32]}},{"componentType":"input","name":"item_input","params":{"shape":[1,78]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":32,"outFeatures":88}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":88,"outFeatures":88}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":78,"outFeatures":88}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":88,"outFeatures":88}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":25344}} {"id":"gen-grow-2389","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2389\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":8}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":8,\"outFeatures\":8}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":8,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":1034}},{"type":"update_params","name":"fc2","params":{"inFeatures":1034,"outFeatures":1034}},{"type":"update_params","name":"head","params":{"inFeatures":1034,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":1211848}} {"id":"gen-mlp-2390","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 82-feature input with 126 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2390\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,82]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":82,"outFeatures":110}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":110,"outFeatures":485}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":485,"outFeatures":927}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":927,"outFeatures":693}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":693,"outFeatures":126}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1241694}} {"id":"gen-ae-2391","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1514-dim input: encode down to a 69-dim bottleneck and decode back to 1514. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2391\nComponents:\n - input (input) params={\"shape\":[1,1514]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1514]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1514,"outFeatures":944}},{"componentType":"linear","name":"enc2","params":{"inFeatures":944,"outFeatures":69}},{"componentType":"linear","name":"dec1","params":{"inFeatures":69,"outFeatures":944}},{"componentType":"linear","name":"dec2","params":{"inFeatures":944,"outFeatures":1514}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2988704}} {"id":"gen-cnn-2392","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 98 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2392\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":32,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":98}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":25125}} {"id":"gen-txf-2393","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 500-token sequences with 78 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2393\nComponents:\n - input (input) params={\"shape\":[1,500]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,500]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19771,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":72,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":72,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1491336}} {"id":"gen-gqa-2394","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 449-token sequences with 43 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2394\nComponents:\n - input (input) params={\"shape\":[1,449]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,449]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49125,"embeddingDim":496}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":496,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[496]}},{"componentType":"linear","name":"head","params":{"inFeatures":496,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":24387328}} {"id":"gen-trim-2396","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2453-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2396\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - fc1 (linear) params={\"inFeatures\":228,\"outFeatures\":2453}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2453,\"outFeatures\":2453}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2453,\"outFeatures\":2453}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2453,\"outFeatures\":2453}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2453,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":228,"outFeatures":376}},{"type":"update_params","name":"fc2","params":{"inFeatures":376,"outFeatures":376}},{"type":"update_params","name":"fc3","params":{"inFeatures":376,"outFeatures":376}},{"type":"update_params","name":"fc4","params":{"inFeatures":376,"outFeatures":376}},{"type":"update_params","name":"head","params":{"inFeatures":376,"outFeatures":19}}],"grade":{"pass":true,"score":88,"params":517000}} {"id":"gen-norm-2397","family":"norm","seed":20260716,"spec":"This 111-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2397\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - fc1 (linear) params={\"inFeatures\":111,\"outFeatures\":84}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":84,\"outFeatures\":404}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":404,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":84}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":404}}],"grade":{"pass":true,"score":94,"params":61440}} {"id":"gen-tower-2398","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 140-feature user input and a 123-feature item input, each through its own 2-layer MLP tower ending at width 485, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2398\nComponents:\n - user_input (input) params={\"shape\":[1,140]}\n - item_input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,140]}},{"componentType":"input","name":"item_input","params":{"shape":[1,123]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":140,"outFeatures":485}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":485,"outFeatures":485}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":123,"outFeatures":485}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":485,"outFeatures":485}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":970,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":598975}} {"id":"gen-grow-2399","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (109-feature input, 32 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2399\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - fc1 (linear) params={\"inFeatures\":109,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":109,"outFeatures":1207}},{"type":"update_params","name":"fc2","params":{"inFeatures":1207,"outFeatures":1207}},{"type":"update_params","name":"head","params":{"inFeatures":1207,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":1627036}} {"id":"gen-mlp-2400","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 407-feature input with 40 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2400\nComponents:\n - input (input) params={\"shape\":[1,407]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,407]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":407,"outFeatures":32}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":32,"outFeatures":425}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":425,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":43624}} {"id":"gen-ae-2401","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2040-dim input: encode down to a 199-dim bottleneck and decode back to 2040. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2401\nComponents:\n - input (input) params={\"shape\":[1,2040]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2040]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2040,"outFeatures":527}},{"componentType":"linear","name":"enc2","params":{"inFeatures":527,"outFeatures":199}},{"componentType":"linear","name":"dec1","params":{"inFeatures":199,"outFeatures":527}},{"componentType":"linear","name":"dec2","params":{"inFeatures":527,"outFeatures":2040}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2359906}} {"id":"gen-cnn-2402","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 80 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2402\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":11,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":8,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":53,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":9145}} {"id":"gen-txf-2403","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 436-token sequences with 82 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2403\nComponents:\n - input (input) params={\"shape\":[1,436]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,436]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34387,"embeddingDim":52}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":52,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":52,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":52,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1824836}} {"id":"gen-gqa-2404","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 423-token sequences with 53 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2404\nComponents:\n - input (input) params={\"shape\":[1,423]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,423]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37005,"embeddingDim":200}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[200]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":200,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[200]}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7411600}} {"id":"gen-trim-2406","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2926-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2406\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":2926}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2926,\"outFeatures\":2926}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2926,\"outFeatures\":2926}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2926,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":95,"outFeatures":175}},{"type":"update_params","name":"fc2","params":{"inFeatures":175,"outFeatures":175}},{"type":"update_params","name":"fc3","params":{"inFeatures":175,"outFeatures":175}},{"type":"update_params","name":"head","params":{"inFeatures":175,"outFeatures":12}}],"grade":{"pass":true,"score":84,"params":79975}} {"id":"gen-norm-2407","family":"norm","seed":20260716,"spec":"This 171-feature, 27-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2407\nComponents:\n - input (input) params={\"shape\":[1,171]}\n - fc1 (linear) params={\"inFeatures\":171,\"outFeatures\":132}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":132,\"outFeatures\":223}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":223,\"outFeatures\":43}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":43,\"outFeatures\":342}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":342,\"outFeatures\":145}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":145,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":132}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":223}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":43}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":342}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":145}}],"grade":{"pass":true,"score":100,"params":129808}} {"id":"gen-tower-2408","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 73-feature user input and a 103-feature item input, each through its own 2-layer MLP tower ending at width 129, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2408\nComponents:\n - user_input (input) params={\"shape\":[1,73]}\n - item_input (input) params={\"shape\":[1,103]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,73]}},{"componentType":"input","name":"item_input","params":{"shape":[1,103]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":73,"outFeatures":129}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":129,"outFeatures":129}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":103,"outFeatures":129}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":129,"outFeatures":129}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":258,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":56244}} {"id":"gen-grow-2409","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (57-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2409\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":57,"outFeatures":1390}},{"type":"update_params","name":"fc2","params":{"inFeatures":1390,"outFeatures":1390}},{"type":"update_params","name":"head","params":{"inFeatures":1390,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":2064150}} {"id":"gen-mlp-2410","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 360-feature input with 28 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2410\nComponents:\n - input (input) params={\"shape\":[1,360]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,360]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":360,"outFeatures":924}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":924,"outFeatures":242}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":242,"outFeatures":582}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":582,"outFeatures":744}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":744,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1150932}} {"id":"gen-ae-2411","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 403-dim input: encode down to a 79-dim bottleneck and decode back to 403. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2411\nComponents:\n - input (input) params={\"shape\":[1,403]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,403]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":403,"outFeatures":997}},{"componentType":"linear","name":"enc2","params":{"inFeatures":997,"outFeatures":79}},{"componentType":"linear","name":"dec1","params":{"inFeatures":79,"outFeatures":997}},{"componentType":"linear","name":"dec2","params":{"inFeatures":997,"outFeatures":403}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":961108}} {"id":"gen-cnn-2412","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 29 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2412\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":63,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":19,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37021}} {"id":"gen-txf-2413","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 392-token sequences with 72 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2413\nComponents:\n - input (input) params={\"shape\":[1,392]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,392]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14645,"embeddingDim":50}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":50,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":50,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":50,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":755850}} {"id":"gen-gqa-2414","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 208-token sequences with 77 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2414\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,208]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36365,"embeddingDim":528}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":528,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[528]}},{"componentType":"linear","name":"head","params":{"inFeatures":528,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":19241376}} {"id":"gen-trim-2416","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1541-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2416\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":1541}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1541,\"outFeatures\":1541}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1541,\"outFeatures\":1541}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1541,\"outFeatures\":1541}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1541,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":82,"outFeatures":277}},{"type":"update_params","name":"fc2","params":{"inFeatures":277,"outFeatures":277}},{"type":"update_params","name":"fc3","params":{"inFeatures":277,"outFeatures":277}},{"type":"update_params","name":"fc4","params":{"inFeatures":277,"outFeatures":277}},{"type":"update_params","name":"head","params":{"inFeatures":277,"outFeatures":9}}],"grade":{"pass":true,"score":88,"params":255394}} {"id":"gen-norm-2417","family":"norm","seed":20260716,"spec":"This 220-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2417\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":253}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":253,\"outFeatures\":97}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":97,\"outFeatures\":223}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":223,\"outFeatures\":285}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":285,\"outFeatures\":238}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":238,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":253}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":97}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":223}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":285}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":238}}],"grade":{"pass":true,"score":100,"params":243927}} {"id":"gen-tower-2418","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 36-feature user input and a 45-feature item input, each through its own 2-layer MLP tower ending at width 117, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2418\nComponents:\n - user_input (input) params={\"shape\":[1,36]}\n - item_input (input) params={\"shape\":[1,45]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,36]}},{"componentType":"input","name":"item_input","params":{"shape":[1,45]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":36,"outFeatures":117}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":117,"outFeatures":117}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":45,"outFeatures":117}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":117,"outFeatures":117}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":234,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":37089}} {"id":"gen-grow-2419","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (126-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2419\nComponents:\n - input (input) params={\"shape\":[1,126]}\n - fc1 (linear) params={\"inFeatures\":126,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":126,"outFeatures":1066}},{"type":"update_params","name":"fc2","params":{"inFeatures":1066,"outFeatures":1066}},{"type":"update_params","name":"head","params":{"inFeatures":1066,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1299454}} {"id":"gen-mlp-2420","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 435-feature input with 156 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2420\nComponents:\n - input (input) params={\"shape\":[1,435]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,435]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":435,"outFeatures":207}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":207,"outFeatures":696}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":696,"outFeatures":368}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":368,"outFeatures":97}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":97,"outFeatures":1012}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1012,"outFeatures":156}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":781977}} {"id":"gen-ae-2421","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 376-dim input: encode down to a 35-dim bottleneck and decode back to 376. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2421\nComponents:\n - input (input) params={\"shape\":[1,376]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,376]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":376,"outFeatures":161}},{"componentType":"linear","name":"enc2","params":{"inFeatures":161,"outFeatures":35}},{"componentType":"linear","name":"dec1","params":{"inFeatures":35,"outFeatures":161}},{"componentType":"linear","name":"dec2","params":{"inFeatures":161,"outFeatures":376}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":132342}} {"id":"gen-cnn-2422","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 37 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2422\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":50,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":63,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":53,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":87507}} {"id":"gen-txf-2423","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 484-token sequences with 71 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2423\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32030,"embeddingDim":488}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":488,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":488,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":488,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":18523016}} {"id":"gen-gqa-2424","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 128-token sequences with 46 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2424\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37000,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6816464}} {"id":"gen-fix-2425","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (204) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2425\nComponents:\n - input (input) params={\"shape\":[1,243]}\n - embed (embedding) params={\"numEmbeddings\":33873,\"embeddingDim\":204}\n - attn (multiHeadAttention) params={\"embedDim\":204,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":204,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":7090836}} {"id":"gen-trim-2426","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3106-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2426\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":3106}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3106,\"outFeatures\":3106}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3106,\"outFeatures\":3106}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3106,\"outFeatures\":3106}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3106,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":368}},{"type":"update_params","name":"fc2","params":{"inFeatures":368,"outFeatures":368}},{"type":"update_params","name":"fc3","params":{"inFeatures":368,"outFeatures":368}},{"type":"update_params","name":"fc4","params":{"inFeatures":368,"outFeatures":368}},{"type":"update_params","name":"head","params":{"inFeatures":368,"outFeatures":4}}],"grade":{"pass":true,"score":88,"params":429824}} {"id":"gen-norm-2427","family":"norm","seed":20260716,"spec":"This 220-feature, 82-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2427\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":326}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":326,\"outFeatures\":466}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":466,\"outFeatures\":339}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":339,\"outFeatures\":82}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":326}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":466}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":339}}],"grade":{"pass":true,"score":100,"params":409408}} {"id":"gen-tower-2428","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 244-feature user input and a 224-feature item input, each through its own 2-layer MLP tower ending at width 256, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2428\nComponents:\n - user_input (input) params={\"shape\":[1,244]}\n - item_input (input) params={\"shape\":[1,224]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,244]}},{"componentType":"input","name":"item_input","params":{"shape":[1,224]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":244,"outFeatures":256}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":256,"outFeatures":256}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":224,"outFeatures":256}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":256,"outFeatures":256}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":512,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":251392}} {"id":"gen-grow-2429","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (78-feature input, 13 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2429\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - fc1 (linear) params={\"inFeatures\":78,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":78,"outFeatures":911}},{"type":"update_params","name":"fc2","params":{"inFeatures":911,"outFeatures":911}},{"type":"update_params","name":"head","params":{"inFeatures":911,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":912822}} {"id":"gen-mlp-2430","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 240-feature input with 5 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2430\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,240]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":240,"outFeatures":925}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":925,"outFeatures":236}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":236,"outFeatures":401}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":401,"outFeatures":713}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":713,"outFeatures":896}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":896,"outFeatures":866}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":866,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":2239963}} {"id":"gen-ae-2431","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1156-dim input: encode down to a 182-dim bottleneck and decode back to 1156. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2431\nComponents:\n - input (input) params={\"shape\":[1,1156]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1156]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1156,"outFeatures":449}},{"componentType":"linear","name":"enc2","params":{"inFeatures":449,"outFeatures":182}},{"componentType":"linear","name":"dec1","params":{"inFeatures":182,"outFeatures":449}},{"componentType":"linear","name":"dec2","params":{"inFeatures":449,"outFeatures":1156}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1201524}} {"id":"gen-cnn-2432","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x61x61 image with 10 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2432\nComponents:\n - input (input) params={\"shape\":[1,3,61,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,61,61]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":22,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":10578}} {"id":"gen-txf-2433","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 141-token sequences with 91 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2433\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,141]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15454,"embeddingDim":62}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":62,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":62,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":994542}} {"id":"gen-gqa-2434","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 433-token sequences with 3 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2434\nComponents:\n - input (input) params={\"shape\":[1,433]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,433]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45139,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5778176}} {"id":"gen-fix-2435","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (322) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2435\nComponents:\n - input (input) params={\"shape\":[1,199]}\n - embed (embedding) params={\"numEmbeddings\":37060,\"embeddingDim\":322}\n - attn (multiHeadAttention) params={\"embedDim\":322,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":322,\"outFeatures\":66}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":12369308}} {"id":"gen-trim-2436","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1680-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2436\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - fc1 (linear) params={\"inFeatures\":109,\"outFeatures\":1680}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1680,\"outFeatures\":1680}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1680,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":109,"outFeatures":286}},{"type":"update_params","name":"fc2","params":{"inFeatures":286,"outFeatures":286}},{"type":"update_params","name":"head","params":{"inFeatures":286,"outFeatures":30}}],"grade":{"pass":true,"score":80,"params":121550}} {"id":"gen-norm-2437","family":"norm","seed":20260716,"spec":"This 94-feature, 50-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2437\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - fc1 (linear) params={\"inFeatures\":94,\"outFeatures\":204}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":204,\"outFeatures\":506}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":506,\"outFeatures\":69}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":69,\"outFeatures\":294}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":294,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":204}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":506}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":69}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":294}}],"grade":{"pass":true,"score":100,"params":192300}} {"id":"gen-tower-2438","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 35-feature user input and a 223-feature item input, each through its own 2-layer MLP tower ending at width 203, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2438\nComponents:\n - user_input (input) params={\"shape\":[1,35]}\n - item_input (input) params={\"shape\":[1,223]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,35]}},{"componentType":"input","name":"item_input","params":{"shape":[1,223]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":35,"outFeatures":203}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":203,"outFeatures":203}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":223,"outFeatures":203}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":203,"outFeatures":203}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":406,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":135198}} {"id":"gen-grow-2439","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (68-feature input, 12 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2439\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":68,"outFeatures":1348}},{"type":"update_params","name":"fc2","params":{"inFeatures":1348,"outFeatures":1348}},{"type":"update_params","name":"head","params":{"inFeatures":1348,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":1924944}} {"id":"gen-mlp-2440","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 377-feature input with 19 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2440\nComponents:\n - input (input) params={\"shape\":[1,377]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,377]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":377,"outFeatures":605}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":605,"outFeatures":221}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":221,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":365989}} {"id":"gen-ae-2441","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1799-dim input: encode down to a 67-dim bottleneck and decode back to 1799. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2441\nComponents:\n - input (input) params={\"shape\":[1,1799]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1799]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1799,"outFeatures":730}},{"componentType":"linear","name":"enc2","params":{"inFeatures":730,"outFeatures":67}},{"componentType":"linear","name":"dec1","params":{"inFeatures":67,"outFeatures":730}},{"componentType":"linear","name":"dec2","params":{"inFeatures":730,"outFeatures":1799}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2724360}} {"id":"gen-cnn-2442","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 51 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2442\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":35,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":40,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":59,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39927}} {"id":"gen-txf-2443","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 512-token sequences with 25 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2443\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15097,"embeddingDim":80}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":80,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":80,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1286560}} {"id":"gen-gqa-2444","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 377-token sequences with 17 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2444\nComponents:\n - input (input) params={\"shape\":[1,377]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,377]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48546,"embeddingDim":720}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":720,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[720]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":720,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[720]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":720,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[720]}},{"componentType":"linear","name":"head","params":{"inFeatures":720,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":34965360}} {"id":"gen-fix-2445","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (74) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2445\nComponents:\n - input (input) params={\"shape\":[1,337]}\n - embed (embedding) params={\"numEmbeddings\":28682,\"embeddingDim\":74}\n - attn (multiHeadAttention) params={\"embedDim\":74,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":74,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2146518}} {"id":"gen-trim-2446","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2792-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2446\nComponents:\n - input (input) params={\"shape\":[1,160]}\n - fc1 (linear) params={\"inFeatures\":160,\"outFeatures\":2792}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2792,\"outFeatures\":2792}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2792,\"outFeatures\":2792}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2792,\"outFeatures\":2792}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2792,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":160,"outFeatures":265}},{"type":"update_params","name":"fc2","params":{"inFeatures":265,"outFeatures":265}},{"type":"update_params","name":"fc3","params":{"inFeatures":265,"outFeatures":265}},{"type":"update_params","name":"fc4","params":{"inFeatures":265,"outFeatures":265}},{"type":"update_params","name":"head","params":{"inFeatures":265,"outFeatures":20}}],"grade":{"pass":true,"score":88,"params":258375}} {"id":"gen-norm-2447","family":"norm","seed":20260716,"spec":"This 20-feature, 37-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2447\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":337}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":337,\"outFeatures\":473}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":473,\"outFeatures\":510}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":510,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":337}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":473}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":510}}],"grade":{"pass":true,"score":100,"params":426241}} {"id":"gen-tower-2448","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 195-feature user input and a 85-feature item input, each through its own 2-layer MLP tower ending at width 281, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2448\nComponents:\n - user_input (input) params={\"shape\":[1,195]}\n - item_input (input) params={\"shape\":[1,85]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,195]}},{"componentType":"input","name":"item_input","params":{"shape":[1,85]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":195,"outFeatures":281}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":281,"outFeatures":281}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":85,"outFeatures":281}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":281,"outFeatures":281}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":562,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":237164}} {"id":"gen-grow-2449","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (47-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2449\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":47,"outFeatures":779}},{"type":"update_params","name":"fc2","params":{"inFeatures":779,"outFeatures":779}},{"type":"update_params","name":"head","params":{"inFeatures":779,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":672277}} {"id":"gen-mlp-2450","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 207-feature input with 161 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2450\nComponents:\n - input (input) params={\"shape\":[1,207]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,207]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":207,"outFeatures":362}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":362,"outFeatures":155}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":155,"outFeatures":110}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":110,"outFeatures":212}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":212,"outFeatures":324}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":324,"outFeatures":138}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":138,"outFeatures":161}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":307032}} {"id":"gen-ae-2451","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2044-dim input: encode down to a 108-dim bottleneck and decode back to 2044. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2451\nComponents:\n - input (input) params={\"shape\":[1,2044]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2044]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2044,"outFeatures":362}},{"componentType":"linear","name":"enc2","params":{"inFeatures":362,"outFeatures":108}},{"componentType":"linear","name":"dec1","params":{"inFeatures":108,"outFeatures":362}},{"componentType":"linear","name":"dec2","params":{"inFeatures":362,"outFeatures":2044}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1558048}} {"id":"gen-cnn-2452","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 33 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2452\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":10131}} {"id":"gen-txf-2453","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 392-token sequences with 99 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2453\nComponents:\n - input (input) params={\"shape\":[1,392]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,392]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9406,"embeddingDim":100}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":100,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":100,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":100,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1070500}} {"id":"gen-gqa-2454","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 437-token sequences with 25 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2454\nComponents:\n - input (input) params={\"shape\":[1,437]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,437]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36648,"embeddingDim":140}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":140,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[140]}},{"componentType":"linear","name":"head","params":{"inFeatures":140,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5134220}} {"id":"gen-trim-2456","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2112-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2456\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":2112}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2112,\"outFeatures\":2112}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2112,\"outFeatures\":2112}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2112,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":88,"outFeatures":290}},{"type":"update_params","name":"fc2","params":{"inFeatures":290,"outFeatures":290}},{"type":"update_params","name":"fc3","params":{"inFeatures":290,"outFeatures":290}},{"type":"update_params","name":"head","params":{"inFeatures":290,"outFeatures":27}}],"grade":{"pass":true,"score":84,"params":201550}} {"id":"gen-norm-2457","family":"norm","seed":20260716,"spec":"This 65-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2457\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":413}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":413,\"outFeatures\":212}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":212,\"outFeatures\":375}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":375,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":413}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":212}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":375}}],"grade":{"pass":true,"score":100,"params":221651}} {"id":"gen-tower-2458","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 188-feature user input and a 195-feature item input, each through its own 2-layer MLP tower ending at width 92, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2458\nComponents:\n - user_input (input) params={\"shape\":[1,188]}\n - item_input (input) params={\"shape\":[1,195]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,188]}},{"componentType":"input","name":"item_input","params":{"shape":[1,195]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":188,"outFeatures":92}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":92,"outFeatures":92}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":195,"outFeatures":92}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":92,"outFeatures":92}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":52348}} {"id":"gen-grow-2459","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (88-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2459\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":88,"outFeatures":1175}},{"type":"update_params","name":"fc2","params":{"inFeatures":1175,"outFeatures":1175}},{"type":"update_params","name":"head","params":{"inFeatures":1175,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1488725}} {"id":"gen-mlp-2460","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 128-feature input with 131 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2460\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,128]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":128,"outFeatures":972}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":972,"outFeatures":600}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":600,"outFeatures":126}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":126,"outFeatures":991}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":991,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1037903}} {"id":"gen-ae-2461","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 737-dim input: encode down to a 108-dim bottleneck and decode back to 737. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2461\nComponents:\n - input (input) params={\"shape\":[1,737]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,737]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":737,"outFeatures":540}},{"componentType":"linear","name":"enc2","params":{"inFeatures":540,"outFeatures":108}},{"componentType":"linear","name":"dec1","params":{"inFeatures":108,"outFeatures":540}},{"componentType":"linear","name":"dec2","params":{"inFeatures":540,"outFeatures":737}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":912600}} {"id":"gen-cnn-2462","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 33 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2462\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":18,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":2826}} {"id":"gen-txf-2463","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 342-token sequences with 38 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2463\nComponents:\n - input (input) params={\"shape\":[1,342]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,342]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15209,"embeddingDim":368}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":368,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6152592}} {"id":"gen-gqa-2464","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 215-token sequences with 10 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2464\nComponents:\n - input (input) params={\"shape\":[1,215]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,215]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12049,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":10}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2122384}} {"id":"gen-fix-2465","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (360) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2465\nComponents:\n - input (input) params={\"shape\":[1,405]}\n - embed (embedding) params={\"numEmbeddings\":8772,\"embeddingDim\":360}\n - attn (multiHeadAttention) params={\"embedDim\":360,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":360,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":3682440}} {"id":"gen-trim-2466","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3750-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2466\nComponents:\n - input (input) params={\"shape\":[1,250]}\n - fc1 (linear) params={\"inFeatures\":250,\"outFeatures\":3750}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3750,\"outFeatures\":3750}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3750,\"outFeatures\":3750}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3750,\"outFeatures\":3750}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3750,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":250,"outFeatures":339}},{"type":"update_params","name":"fc2","params":{"inFeatures":339,"outFeatures":339}},{"type":"update_params","name":"fc3","params":{"inFeatures":339,"outFeatures":339}},{"type":"update_params","name":"fc4","params":{"inFeatures":339,"outFeatures":339}},{"type":"update_params","name":"head","params":{"inFeatures":339,"outFeatures":42}}],"grade":{"pass":true,"score":88,"params":443751}} {"id":"gen-norm-2467","family":"norm","seed":20260716,"spec":"This 78-feature, 86-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2467\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - fc1 (linear) params={\"inFeatures\":78,\"outFeatures\":196}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":196,\"outFeatures\":313}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":313,\"outFeatures\":417}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":417,\"outFeatures\":443}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":443,\"outFeatures\":86}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":196}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":313}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":417}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":443}}],"grade":{"pass":true,"score":100,"params":429986}} {"id":"gen-tower-2468","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 99-feature user input and a 92-feature item input, each through its own 2-layer MLP tower ending at width 118, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2468\nComponents:\n - user_input (input) params={\"shape\":[1,99]}\n - item_input (input) params={\"shape\":[1,92]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,99]}},{"componentType":"input","name":"item_input","params":{"shape":[1,92]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":99,"outFeatures":118}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":118,"outFeatures":118}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":92,"outFeatures":118}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":118,"outFeatures":118}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":236,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":50622}} {"id":"gen-grow-2469","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (99-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2469\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - fc1 (linear) params={\"inFeatures\":99,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":99,"outFeatures":1173}},{"type":"update_params","name":"fc2","params":{"inFeatures":1173,"outFeatures":1173}},{"type":"update_params","name":"head","params":{"inFeatures":1173,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":1526073}} {"id":"gen-mlp-2470","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 60-feature input with 131 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2470\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,60]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":60,"outFeatures":749}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":749,"outFeatures":284}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":284,"outFeatures":604}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":604,"outFeatures":16}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":131}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":440952}} {"id":"gen-ae-2471","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1637-dim input: encode down to a 169-dim bottleneck and decode back to 1637. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2471\nComponents:\n - input (input) params={\"shape\":[1,1637]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1637]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1637,"outFeatures":668}},{"componentType":"linear","name":"enc2","params":{"inFeatures":668,"outFeatures":169}},{"componentType":"linear","name":"dec1","params":{"inFeatures":169,"outFeatures":668}},{"componentType":"linear","name":"dec2","params":{"inFeatures":668,"outFeatures":1637}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2412816}} {"id":"gen-cnn-2472","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x60x60 image with 40 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2472\nComponents:\n - input (input) params={\"shape\":[1,3,60,60]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,60,60]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":21,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":17,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":13355}} {"id":"gen-txf-2473","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 317-token sequences with 69 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2473\nComponents:\n - input (input) params={\"shape\":[1,317]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,317]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10985,"embeddingDim":20}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":20,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":222680}} {"id":"gen-gqa-2474","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 278-token sequences with 67 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2474\nComponents:\n - input (input) params={\"shape\":[1,278]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,278]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19948,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":128,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2561920}} {"id":"gen-trim-2476","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2371-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2476\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - fc1 (linear) params={\"inFeatures\":164,\"outFeatures\":2371}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2371,\"outFeatures\":2371}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2371,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":164,"outFeatures":92}},{"type":"update_params","name":"fc2","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"head","params":{"inFeatures":92,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":25300}} {"id":"gen-norm-2477","family":"norm","seed":20260716,"spec":"This 149-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2477\nComponents:\n - input (input) params={\"shape\":[1,149]}\n - fc1 (linear) params={\"inFeatures\":149,\"outFeatures\":305}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":305,\"outFeatures\":111}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":111,\"outFeatures\":427}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":427,\"outFeatures\":394}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":394,\"outFeatures\":420}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":420,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":305}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":111}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":427}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":394}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":420}}],"grade":{"pass":true,"score":100,"params":488555}} {"id":"gen-tower-2478","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 189-feature user input and a 113-feature item input, each through its own 2-layer MLP tower ending at width 139, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2478\nComponents:\n - user_input (input) params={\"shape\":[1,189]}\n - item_input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,189]}},{"componentType":"input","name":"item_input","params":{"shape":[1,113]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":189,"outFeatures":139}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":139,"outFeatures":139}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":113,"outFeatures":139}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":139,"outFeatures":139}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":278,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":80898}} {"id":"gen-grow-2479","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (38-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2479\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":1059}},{"type":"update_params","name":"fc2","params":{"inFeatures":1059,"outFeatures":1059}},{"type":"update_params","name":"head","params":{"inFeatures":1059,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":1163841}} {"id":"gen-mlp-2480","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 510-feature input with 91 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2480\nComponents:\n - input (input) params={\"shape\":[1,510]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,510]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":510,"outFeatures":213}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":213,"outFeatures":1004}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1004,"outFeatures":494}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":494,"outFeatures":846}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":846,"outFeatures":777}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":777,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1964431}} {"id":"gen-ae-2481","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 912-dim input: encode down to a 157-dim bottleneck and decode back to 912. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2481\nComponents:\n - input (input) params={\"shape\":[1,912]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,912]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":912,"outFeatures":126}},{"componentType":"linear","name":"enc2","params":{"inFeatures":126,"outFeatures":157}},{"componentType":"linear","name":"dec1","params":{"inFeatures":157,"outFeatures":126}},{"componentType":"linear","name":"dec2","params":{"inFeatures":126,"outFeatures":912}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":269388}} {"id":"gen-cnn-2482","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x51x51 image with 12 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2482\nComponents:\n - input (input) params={\"shape\":[1,3,51,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,51,51]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":16,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":35,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":13326}} {"id":"gen-txf-2483","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 280-token sequences with 62 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2483\nComponents:\n - input (input) params={\"shape\":[1,280]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,280]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49257,"embeddingDim":512}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":512,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":512,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":26299904}} {"id":"gen-gqa-2484","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 209-token sequences with 66 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2484\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,209]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39082,"embeddingDim":360}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":360,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[360]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":360,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[360]}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":14093280}} {"id":"gen-trim-2486","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3679-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2486\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - fc1 (linear) params={\"inFeatures\":151,\"outFeatures\":3679}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3679,\"outFeatures\":3679}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3679,\"outFeatures\":3679}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3679,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":151,"outFeatures":236}},{"type":"update_params","name":"fc2","params":{"inFeatures":236,"outFeatures":236}},{"type":"update_params","name":"fc3","params":{"inFeatures":236,"outFeatures":236}},{"type":"update_params","name":"head","params":{"inFeatures":236,"outFeatures":32}}],"grade":{"pass":true,"score":84,"params":154580}} {"id":"gen-norm-2487","family":"norm","seed":20260716,"spec":"This 164-feature, 21-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2487\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - fc1 (linear) params={\"inFeatures\":164,\"outFeatures\":163}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":163,\"outFeatures\":122}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":122,\"outFeatures\":450}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":450,\"outFeatures\":132}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":132,\"outFeatures\":119}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":119,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":163}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":122}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":450}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":132}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":119}}],"grade":{"pass":true,"score":100,"params":179125}} {"id":"gen-tower-2488","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 162-feature user input and a 167-feature item input, each through its own 2-layer MLP tower ending at width 65, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2488\nComponents:\n - user_input (input) params={\"shape\":[1,162]}\n - item_input (input) params={\"shape\":[1,167]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,162]}},{"componentType":"input","name":"item_input","params":{"shape":[1,167]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":162,"outFeatures":65}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":65,"outFeatures":65}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":167,"outFeatures":65}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":65,"outFeatures":65}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":130,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29965}} {"id":"gen-grow-2489","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (60-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2489\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":686}},{"type":"update_params","name":"fc2","params":{"inFeatures":686,"outFeatures":686}},{"type":"update_params","name":"head","params":{"inFeatures":686,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":528220}} {"id":"gen-mlp-2490","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 32-feature input with 192 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2490\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,32]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":32,"outFeatures":954}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":954,"outFeatures":645}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":645,"outFeatures":670}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":670,"outFeatures":841}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":841,"outFeatures":192}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1802950}} {"id":"gen-ae-2491","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1924-dim input: encode down to a 69-dim bottleneck and decode back to 1924. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2491\nComponents:\n - input (input) params={\"shape\":[1,1924]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1924]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1924,"outFeatures":301}},{"componentType":"linear","name":"enc2","params":{"inFeatures":301,"outFeatures":69}},{"componentType":"linear","name":"dec1","params":{"inFeatures":69,"outFeatures":301}},{"componentType":"linear","name":"dec2","params":{"inFeatures":301,"outFeatures":1924}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1199786}} {"id":"gen-cnn-2492","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 28 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2492\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":10164}} {"id":"gen-txf-2493","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 75-token sequences with 87 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2493\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,75]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18120,"embeddingDim":114}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":114,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":114,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":114,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2179566}} {"id":"gen-gqa-2494","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 439-token sequences with 8 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2494\nComponents:\n - input (input) params={\"shape\":[1,439]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,439]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10122,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":304,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3079520}} {"id":"gen-fix-2495","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (378) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2495\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - embed (embedding) params={\"numEmbeddings\":42488,\"embeddingDim\":378}\n - attn (multiHeadAttention) params={\"embedDim\":378,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":378,\"outFeatures\":100}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":16669800}} {"id":"gen-trim-2496","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3314-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2496\nComponents:\n - input (input) params={\"shape\":[1,81]}\n - fc1 (linear) params={\"inFeatures\":81,\"outFeatures\":3314}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3314,\"outFeatures\":3314}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3314,\"outFeatures\":3314}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3314,\"outFeatures\":3314}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3314,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":81,"outFeatures":275}},{"type":"update_params","name":"fc2","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"fc3","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"fc4","params":{"inFeatures":275,"outFeatures":275}},{"type":"update_params","name":"head","params":{"inFeatures":275,"outFeatures":45}}],"grade":{"pass":true,"score":88,"params":261525}} {"id":"gen-norm-2497","family":"norm","seed":20260716,"spec":"This 190-feature, 4-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2497\nComponents:\n - input (input) params={\"shape\":[1,190]}\n - fc1 (linear) params={\"inFeatures\":190,\"outFeatures\":410}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":410,\"outFeatures\":304}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":304,\"outFeatures\":62}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":62,\"outFeatures\":425}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":425,\"outFeatures\":236}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":236,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":410}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":304}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":62}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":425}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":236}}],"grade":{"pass":true,"score":100,"params":348982}} {"id":"gen-tower-2498","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 71-feature user input and a 188-feature item input, each through its own 2-layer MLP tower ending at width 509, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2498\nComponents:\n - user_input (input) params={\"shape\":[1,71]}\n - item_input (input) params={\"shape\":[1,188]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,71]}},{"componentType":"input","name":"item_input","params":{"shape":[1,188]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":71,"outFeatures":509}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":509,"outFeatures":509}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":188,"outFeatures":509}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":509,"outFeatures":509}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1018,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":651011}} {"id":"gen-grow-2499","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (52-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2499\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - fc1 (linear) params={\"inFeatures\":52,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":52,"outFeatures":1361}},{"type":"update_params","name":"fc2","params":{"inFeatures":1361,"outFeatures":1361}},{"type":"update_params","name":"head","params":{"inFeatures":1361,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1951674}} {"id":"gen-mlp-2500","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 458-feature input with 157 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2500\nComponents:\n - input (input) params={\"shape\":[1,458]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,458]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":458,"outFeatures":197}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":197,"outFeatures":763}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":763,"outFeatures":157}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":360328}} {"id":"gen-ae-2501","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1550-dim input: encode down to a 130-dim bottleneck and decode back to 1550. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2501\nComponents:\n - input (input) params={\"shape\":[1,1550]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1550]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1550,"outFeatures":664}},{"componentType":"linear","name":"enc2","params":{"inFeatures":664,"outFeatures":130}},{"componentType":"linear","name":"dec1","params":{"inFeatures":130,"outFeatures":664}},{"componentType":"linear","name":"dec2","params":{"inFeatures":664,"outFeatures":1550}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2231040}} {"id":"gen-cnn-2502","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 2 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2502\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":11,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":24675}} {"id":"gen-txf-2503","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 56-token sequences with 32 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2503\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,56]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46706,"embeddingDim":70}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":70,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":70,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3291260}} {"id":"gen-gqa-2504","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 347-token sequences with 85 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2504\nComponents:\n - input (input) params={\"shape\":[1,347]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,347]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12634,"embeddingDim":576}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":576,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[576]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":576,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[576]}},{"componentType":"linear","name":"head","params":{"inFeatures":576,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7326144}} {"id":"gen-fix-2505","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (90) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2505\nComponents:\n - input (input) params={\"shape\":[1,366]}\n - embed (embedding) params={\"numEmbeddings\":31212,\"embeddingDim\":90}\n - attn (multiHeadAttention) params={\"embedDim\":90,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":90,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":2842560}} {"id":"gen-trim-2506","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3862-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2506\nComponents:\n - input (input) params={\"shape\":[1,200]}\n - fc1 (linear) params={\"inFeatures\":200,\"outFeatures\":3862}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3862,\"outFeatures\":3862}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3862,\"outFeatures\":3862}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3862,\"outFeatures\":3862}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3862,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":200,"outFeatures":68}},{"type":"update_params","name":"fc2","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"fc3","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"fc4","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"head","params":{"inFeatures":68,"outFeatures":38}}],"grade":{"pass":true,"score":88,"params":30056}} {"id":"gen-norm-2507","family":"norm","seed":20260716,"spec":"This 141-feature, 5-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2507\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - fc1 (linear) params={\"inFeatures\":141,\"outFeatures\":219}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":219,\"outFeatures\":115}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":115,\"outFeatures\":431}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":431,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":219}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":115}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":431}}],"grade":{"pass":true,"score":100,"params":107784}} {"id":"gen-tower-2508","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 164-feature user input and a 58-feature item input, each through its own 2-layer MLP tower ending at width 188, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2508\nComponents:\n - user_input (input) params={\"shape\":[1,164]}\n - item_input (input) params={\"shape\":[1,58]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,164]}},{"componentType":"input","name":"item_input","params":{"shape":[1,58]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":164,"outFeatures":188}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":188,"outFeatures":188}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":58,"outFeatures":188}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":188,"outFeatures":188}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":112800}} {"id":"gen-grow-2509","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (35-feature input, 48 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2509\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - fc1 (linear) params={\"inFeatures\":35,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":35,"outFeatures":842}},{"type":"update_params","name":"fc2","params":{"inFeatures":842,"outFeatures":842}},{"type":"update_params","name":"head","params":{"inFeatures":842,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":778850}} {"id":"gen-mlp-2510","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 201-feature input with 70 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2510\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,201]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":201,"outFeatures":818}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":818,"outFeatures":720}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":720,"outFeatures":128}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":128,"outFeatures":489}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":489,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":942360}} {"id":"gen-ae-2511","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1426-dim input: encode down to a 53-dim bottleneck and decode back to 1426. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2511\nComponents:\n - input (input) params={\"shape\":[1,1426]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1426]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1426,"outFeatures":469}},{"componentType":"linear","name":"enc2","params":{"inFeatures":469,"outFeatures":53}},{"componentType":"linear","name":"dec1","params":{"inFeatures":53,"outFeatures":469}},{"componentType":"linear","name":"dec2","params":{"inFeatures":469,"outFeatures":1426}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1387302}} {"id":"gen-cnn-2512","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 52 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2512\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":38,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":22,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":12800}} {"id":"gen-txf-2513","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 266-token sequences with 80 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2513\nComponents:\n - input (input) params={\"shape\":[1,266]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,266]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42980,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":48,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2085312}} {"id":"gen-gqa-2514","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 117-token sequences with 96 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2514\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,117]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32228,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":18101440}} {"id":"gen-trim-2516","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3121-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2516\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":3121}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3121,\"outFeatures\":3121}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3121,\"outFeatures\":3121}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3121,\"outFeatures\":3121}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3121,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":66,"outFeatures":353}},{"type":"update_params","name":"fc2","params":{"inFeatures":353,"outFeatures":353}},{"type":"update_params","name":"fc3","params":{"inFeatures":353,"outFeatures":353}},{"type":"update_params","name":"fc4","params":{"inFeatures":353,"outFeatures":353}},{"type":"update_params","name":"head","params":{"inFeatures":353,"outFeatures":23}}],"grade":{"pass":true,"score":88,"params":405244}} {"id":"gen-norm-2517","family":"norm","seed":20260716,"spec":"This 177-feature, 81-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2517\nComponents:\n - input (input) params={\"shape\":[1,177]}\n - fc1 (linear) params={\"inFeatures\":177,\"outFeatures\":162}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":162,\"outFeatures\":249}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":249,\"outFeatures\":54}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":54,\"outFeatures\":120}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":120,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":162}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":249}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":54}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":120}}],"grade":{"pass":true,"score":100,"params":98658}} {"id":"gen-tower-2518","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 231-feature user input and a 134-feature item input, each through its own 2-layer MLP tower ending at width 179, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2518\nComponents:\n - user_input (input) params={\"shape\":[1,231]}\n - item_input (input) params={\"shape\":[1,134]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,231]}},{"componentType":"input","name":"item_input","params":{"shape":[1,134]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":231,"outFeatures":179}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":179,"outFeatures":179}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":134,"outFeatures":179}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":179,"outFeatures":179}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":358,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":129775}} {"id":"gen-grow-2519","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (97-feature input, 32 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2519\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":97,"outFeatures":746}},{"type":"update_params","name":"fc2","params":{"inFeatures":746,"outFeatures":746}},{"type":"update_params","name":"head","params":{"inFeatures":746,"outFeatures":32}}],"grade":{"pass":true,"score":80,"params":652750}} {"id":"gen-mlp-2520","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 389-feature input with 41 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2520\nComponents:\n - input (input) params={\"shape\":[1,389]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,389]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":389,"outFeatures":619}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":619,"outFeatures":911}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":911,"outFeatures":909}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":909,"outFeatures":97}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":97,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1724949}} {"id":"gen-ae-2521","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 256-dim input: encode down to a 28-dim bottleneck and decode back to 256. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2521\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,256]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":256,"outFeatures":565}},{"componentType":"linear","name":"enc2","params":{"inFeatures":565,"outFeatures":28}},{"componentType":"linear","name":"dec1","params":{"inFeatures":28,"outFeatures":565}},{"componentType":"linear","name":"dec2","params":{"inFeatures":565,"outFeatures":256}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":320920}} {"id":"gen-cnn-2522","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x42x42 image with 12 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2522\nComponents:\n - input (input) params={\"shape\":[1,3,42,42]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,42,42]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":20,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":54,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":12}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":69060}} {"id":"gen-txf-2523","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 41-token sequences with 49 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2523\nComponents:\n - input (input) params={\"shape\":[1,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,41]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15659,"embeddingDim":64}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":64,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1021696}} {"id":"gen-gqa-2524","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 452-token sequences with 35 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2524\nComponents:\n - input (input) params={\"shape\":[1,452]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,452]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45740,"embeddingDim":88}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":88,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[88]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":88,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[88]}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":4028200}} {"id":"gen-trim-2526","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3804-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2526\nComponents:\n - input (input) params={\"shape\":[1,145]}\n - fc1 (linear) params={\"inFeatures\":145,\"outFeatures\":3804}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3804,\"outFeatures\":3804}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3804,\"outFeatures\":3804}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3804,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":145,"outFeatures":70}},{"type":"update_params","name":"fc2","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"fc3","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"head","params":{"inFeatures":70,"outFeatures":49}}],"grade":{"pass":true,"score":84,"params":23380}} {"id":"gen-norm-2527","family":"norm","seed":20260716,"spec":"This 202-feature, 90-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2527\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - fc1 (linear) params={\"inFeatures\":202,\"outFeatures\":436}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":436,\"outFeatures\":169}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":169,\"outFeatures\":90}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":436}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":169}}],"grade":{"pass":true,"score":94,"params":176966}} {"id":"gen-tower-2528","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 126-feature user input and a 72-feature item input, each through its own 2-layer MLP tower ending at width 409, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2528\nComponents:\n - user_input (input) params={\"shape\":[1,126]}\n - item_input (input) params={\"shape\":[1,72]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,126]}},{"componentType":"input","name":"item_input","params":{"shape":[1,72]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":126,"outFeatures":409}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":409,"outFeatures":409}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":72,"outFeatures":409}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":409,"outFeatures":409}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":818,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":416362}} {"id":"gen-grow-2529","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (105-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2529\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - fc1 (linear) params={\"inFeatures\":105,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":105,"outFeatures":866}},{"type":"update_params","name":"fc2","params":{"inFeatures":866,"outFeatures":866}},{"type":"update_params","name":"head","params":{"inFeatures":866,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":873794}} {"id":"gen-mlp-2530","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 322-feature input with 182 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2530\nComponents:\n - input (input) params={\"shape\":[1,322]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,322]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":322,"outFeatures":212}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":212,"outFeatures":915}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":915,"outFeatures":182}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":428774}} {"id":"gen-ae-2531","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 668-dim input: encode down to a 70-dim bottleneck and decode back to 668. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2531\nComponents:\n - input (input) params={\"shape\":[1,668]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,668]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":668,"outFeatures":347}},{"componentType":"linear","name":"enc2","params":{"inFeatures":347,"outFeatures":70}},{"componentType":"linear","name":"dec1","params":{"inFeatures":70,"outFeatures":347}},{"componentType":"linear","name":"dec2","params":{"inFeatures":347,"outFeatures":668}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":512172}} {"id":"gen-cnn-2532","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 38 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2532\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":48,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":46,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":48728}} {"id":"gen-txf-2533","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 459-token sequences with 38 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2533\nComponents:\n - input (input) params={\"shape\":[1,459]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,459]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40898,"embeddingDim":216}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":216,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":9028800}} {"id":"gen-gqa-2534","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 100-token sequences with 97 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2534\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,100]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17145,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1793168}} {"id":"gen-fix-2535","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (394) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2535\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - embed (embedding) params={\"numEmbeddings\":20100,\"embeddingDim\":394}\n - attn (multiHeadAttention) params={\"embedDim\":394,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":394,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":8543496}} {"id":"gen-trim-2536","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3305-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2536\nComponents:\n - input (input) params={\"shape\":[1,152]}\n - fc1 (linear) params={\"inFeatures\":152,\"outFeatures\":3305}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3305,\"outFeatures\":3305}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3305,\"outFeatures\":3305}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3305,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":152,"outFeatures":112}},{"type":"update_params","name":"fc2","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"fc3","params":{"inFeatures":112,"outFeatures":112}},{"type":"update_params","name":"head","params":{"inFeatures":112,"outFeatures":28}}],"grade":{"pass":true,"score":84,"params":45248}} {"id":"gen-norm-2537","family":"norm","seed":20260716,"spec":"This 97-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2537\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":135}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":135,\"outFeatures\":372}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":372,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":135}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":372}}],"grade":{"pass":true,"score":94,"params":77451}} {"id":"gen-tower-2538","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 49-feature user input and a 156-feature item input, each through its own 2-layer MLP tower ending at width 433, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2538\nComponents:\n - user_input (input) params={\"shape\":[1,49]}\n - item_input (input) params={\"shape\":[1,156]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,49]}},{"componentType":"input","name":"item_input","params":{"shape":[1,156]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":49,"outFeatures":433}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":433,"outFeatures":433}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":156,"outFeatures":433}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":433,"outFeatures":433}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":866,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":464609}} {"id":"gen-grow-2539","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2539\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":1319}},{"type":"update_params","name":"fc2","params":{"inFeatures":1319,"outFeatures":1319}},{"type":"update_params","name":"head","params":{"inFeatures":1319,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1805711}} {"id":"gen-mlp-2540","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 376-feature input with 24 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2540\nComponents:\n - input (input) params={\"shape\":[1,376]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,376]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":376,"outFeatures":795}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":795,"outFeatures":615}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":615,"outFeatures":80}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":80,"outFeatures":270}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":270,"outFeatures":1014}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":1014,"outFeatures":466}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":466,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1616133}} {"id":"gen-ae-2541","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1168-dim input: encode down to a 145-dim bottleneck and decode back to 1168. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2541\nComponents:\n - input (input) params={\"shape\":[1,1168]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1168]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1168,"outFeatures":201}},{"componentType":"linear","name":"enc2","params":{"inFeatures":201,"outFeatures":145}},{"componentType":"linear","name":"dec1","params":{"inFeatures":145,"outFeatures":201}},{"componentType":"linear","name":"dec2","params":{"inFeatures":201,"outFeatures":1168}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":527826}} {"id":"gen-cnn-2542","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x39x39 image with 39 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2542\nComponents:\n - input (input) params={\"shape\":[1,3,39,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,39,39]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":16,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1896}} {"id":"gen-txf-2543","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 47-token sequences with 65 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2543\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,47]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33553,"embeddingDim":22}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":22,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":22,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":743468}} {"id":"gen-gqa-2544","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 501-token sequences with 76 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2544\nComponents:\n - input (input) params={\"shape\":[1,501]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,501]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19272,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":304,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":76}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5881792}} {"id":"gen-fix-2545","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (346) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2545\nComponents:\n - input (input) params={\"shape\":[1,195]}\n - embed (embedding) params={\"numEmbeddings\":12602,\"embeddingDim\":346}\n - attn (multiHeadAttention) params={\"embedDim\":346,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":346,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4848152}} {"id":"gen-trim-2546","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3746-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2546\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":3746}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3746,\"outFeatures\":3746}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3746,\"outFeatures\":3746}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3746,\"outFeatures\":3746}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3746,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":64}},{"type":"update_params","name":"fc2","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"fc3","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"fc4","params":{"inFeatures":64,"outFeatures":64}},{"type":"update_params","name":"head","params":{"inFeatures":64,"outFeatures":6}}],"grade":{"pass":true,"score":88,"params":14784}} {"id":"gen-norm-2547","family":"norm","seed":20260716,"spec":"This 16-feature, 33-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2547\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":192}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":192,\"outFeatures\":233}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":233,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":192}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":233}}],"grade":{"pass":true,"score":94,"params":55497}} {"id":"gen-tower-2548","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 147-feature user input and a 203-feature item input, each through its own 2-layer MLP tower ending at width 458, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2548\nComponents:\n - user_input (input) params={\"shape\":[1,147]}\n - item_input (input) params={\"shape\":[1,203]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,147]}},{"componentType":"input","name":"item_input","params":{"shape":[1,203]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":147,"outFeatures":458}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":458,"outFeatures":458}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":203,"outFeatures":458}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":458,"outFeatures":458}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":916,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":580744}} {"id":"gen-grow-2549","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (102-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2549\nComponents:\n - input (input) params={\"shape\":[1,102]}\n - fc1 (linear) params={\"inFeatures\":102,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":102,"outFeatures":1188}},{"type":"update_params","name":"fc2","params":{"inFeatures":1188,"outFeatures":1188}},{"type":"update_params","name":"head","params":{"inFeatures":1188,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1564596}} {"id":"gen-mlp-2550","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 122-feature input with 69 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2550\nComponents:\n - input (input) params={\"shape\":[1,122]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,122]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":122,"outFeatures":361}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":361,"outFeatures":193}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":193,"outFeatures":499}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":499,"outFeatures":347}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":347,"outFeatures":444}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":444,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":567879}} {"id":"gen-ae-2551","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1075-dim input: encode down to a 124-dim bottleneck and decode back to 1075. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2551\nComponents:\n - input (input) params={\"shape\":[1,1075]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1075]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1075,"outFeatures":804}},{"componentType":"linear","name":"enc2","params":{"inFeatures":804,"outFeatures":124}},{"componentType":"linear","name":"dec1","params":{"inFeatures":124,"outFeatures":804}},{"componentType":"linear","name":"dec2","params":{"inFeatures":804,"outFeatures":1075}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1927992}} {"id":"gen-cnn-2552","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 29 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2552\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":21,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":41595}} {"id":"gen-txf-2553","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 170-token sequences with 6 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2553\nComponents:\n - input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,170]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34518,"embeddingDim":28}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":28,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":969808}} {"id":"gen-gqa-2554","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 344-token sequences with 41 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2554\nComponents:\n - input (input) params={\"shape\":[1,344]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,344]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33175,"embeddingDim":168}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":168,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[168]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":168,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[168]}},{"componentType":"linear","name":"head","params":{"inFeatures":168,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5580288}} {"id":"gen-trim-2556","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3987-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2556\nComponents:\n - input (input) params={\"shape\":[1,54]}\n - fc1 (linear) params={\"inFeatures\":54,\"outFeatures\":3987}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3987,\"outFeatures\":3987}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3987,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":54,"outFeatures":90}},{"type":"update_params","name":"fc2","params":{"inFeatures":90,"outFeatures":90}},{"type":"update_params","name":"head","params":{"inFeatures":90,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":15570}} {"id":"gen-norm-2557","family":"norm","seed":20260716,"spec":"This 239-feature, 83-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2557\nComponents:\n - input (input) params={\"shape\":[1,239]}\n - fc1 (linear) params={\"inFeatures\":239,\"outFeatures\":498}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":498,\"outFeatures\":150}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":150,\"outFeatures\":83}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":498}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":150}}],"grade":{"pass":true,"score":94,"params":206172}} {"id":"gen-tower-2558","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 105-feature user input and a 140-feature item input, each through its own 2-layer MLP tower ending at width 451, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2558\nComponents:\n - user_input (input) params={\"shape\":[1,105]}\n - item_input (input) params={\"shape\":[1,140]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,105]}},{"componentType":"input","name":"item_input","params":{"shape":[1,140]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":105,"outFeatures":451}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":451,"outFeatures":451}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":140,"outFeatures":451}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":451,"outFeatures":451}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":902,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":518199}} {"id":"gen-grow-2559","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (88-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2559\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":88,"outFeatures":647}},{"type":"update_params","name":"fc2","params":{"inFeatures":647,"outFeatures":647}},{"type":"update_params","name":"head","params":{"inFeatures":647,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":482662}} {"id":"gen-mlp-2560","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 439-feature input with 132 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2560\nComponents:\n - input (input) params={\"shape\":[1,439]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,439]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":439,"outFeatures":266}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":266,"outFeatures":364}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":364,"outFeatures":132}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":261646}} {"id":"gen-ae-2561","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 731-dim input: encode down to a 168-dim bottleneck and decode back to 731. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2561\nComponents:\n - input (input) params={\"shape\":[1,731]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,731]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":731,"outFeatures":902}},{"componentType":"linear","name":"enc2","params":{"inFeatures":902,"outFeatures":168}},{"componentType":"linear","name":"dec1","params":{"inFeatures":168,"outFeatures":902}},{"componentType":"linear","name":"dec2","params":{"inFeatures":902,"outFeatures":731}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1621796}} {"id":"gen-cnn-2562","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x36x36 image with 80 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2562\nComponents:\n - input (input) params={\"shape\":[1,3,36,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,36,36]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":35,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":24,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30738}} {"id":"gen-txf-2563","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 132-token sequences with 24 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2563\nComponents:\n - input (input) params={\"shape\":[1,132]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,132]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20358,"embeddingDim":128}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":128,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":128,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":24}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2739968}} {"id":"gen-gqa-2564","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 228-token sequences with 23 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2564\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10401,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":368,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[368]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":368,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3836032}} {"id":"gen-fix-2565","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (316) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2565\nComponents:\n - input (input) params={\"shape\":[1,287]}\n - embed (embedding) params={\"numEmbeddings\":18982,\"embeddingDim\":316}\n - attn (multiHeadAttention) params={\"embedDim\":316,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":316,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":6412588}} {"id":"gen-trim-2566","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2742-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2566\nComponents:\n - input (input) params={\"shape\":[1,88]}\n - fc1 (linear) params={\"inFeatures\":88,\"outFeatures\":2742}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2742,\"outFeatures\":2742}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2742,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":88,"outFeatures":352}},{"type":"update_params","name":"fc2","params":{"inFeatures":352,"outFeatures":352}},{"type":"update_params","name":"head","params":{"inFeatures":352,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":161920}} {"id":"gen-norm-2567","family":"norm","seed":20260716,"spec":"This 98-feature, 60-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2567\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":301}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":301,\"outFeatures\":383}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":383,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":301}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":383}}],"grade":{"pass":true,"score":94,"params":167761}} {"id":"gen-tower-2568","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 153-feature user input and a 196-feature item input, each through its own 2-layer MLP tower ending at width 437, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2568\nComponents:\n - user_input (input) params={\"shape\":[1,153]}\n - item_input (input) params={\"shape\":[1,196]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,153]}},{"componentType":"input","name":"item_input","params":{"shape":[1,196]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":153,"outFeatures":437}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":437,"outFeatures":437}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":196,"outFeatures":437}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":437,"outFeatures":437}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":874,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":535325}} {"id":"gen-grow-2569","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (32-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2569\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":1279}},{"type":"update_params","name":"fc2","params":{"inFeatures":1279,"outFeatures":1279}},{"type":"update_params","name":"head","params":{"inFeatures":1279,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":1729208}} {"id":"gen-mlp-2570","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 237-feature input with 174 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2570\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,237]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":237,"outFeatures":542}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":542,"outFeatures":801}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":801,"outFeatures":174}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":701970}} {"id":"gen-ae-2571","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1269-dim input: encode down to a 97-dim bottleneck and decode back to 1269. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2571\nComponents:\n - input (input) params={\"shape\":[1,1269]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1269]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1269,"outFeatures":853}},{"componentType":"linear","name":"enc2","params":{"inFeatures":853,"outFeatures":97}},{"componentType":"linear","name":"dec1","params":{"inFeatures":97,"outFeatures":853}},{"componentType":"linear","name":"dec2","params":{"inFeatures":853,"outFeatures":1269}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2330396}} {"id":"gen-cnn-2572","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x41x41 image with 43 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2572\nComponents:\n - input (input) params={\"shape\":[1,3,41,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,41,41]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":57,"outChannels":11,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":11,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":21865}} {"id":"gen-txf-2573","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 57-token sequences with 23 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2573\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,57]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24754,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":240,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":240,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":6637680}} {"id":"gen-gqa-2574","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 55-token sequences with 72 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2574\nComponents:\n - input (input) params={\"shape\":[1,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,55]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36819,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3836664}} {"id":"gen-trim-2576","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3640-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2576\nComponents:\n - input (input) params={\"shape\":[1,186]}\n - fc1 (linear) params={\"inFeatures\":186,\"outFeatures\":3640}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3640,\"outFeatures\":3640}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3640,\"outFeatures\":3640}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3640,\"outFeatures\":3640}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3640,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":186,"outFeatures":238}},{"type":"update_params","name":"fc2","params":{"inFeatures":238,"outFeatures":238}},{"type":"update_params","name":"fc3","params":{"inFeatures":238,"outFeatures":238}},{"type":"update_params","name":"fc4","params":{"inFeatures":238,"outFeatures":238}},{"type":"update_params","name":"head","params":{"inFeatures":238,"outFeatures":20}}],"grade":{"pass":true,"score":88,"params":218960}} {"id":"gen-norm-2577","family":"norm","seed":20260716,"spec":"This 217-feature, 14-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2577\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - fc1 (linear) params={\"inFeatures\":217,\"outFeatures\":84}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":84,\"outFeatures\":133}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":133,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":84}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":133}}],"grade":{"pass":true,"score":94,"params":31262}} {"id":"gen-tower-2578","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 89-feature user input and a 171-feature item input, each through its own 2-layer MLP tower ending at width 151, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2578\nComponents:\n - user_input (input) params={\"shape\":[1,89]}\n - item_input (input) params={\"shape\":[1,171]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,89]}},{"componentType":"input","name":"item_input","params":{"shape":[1,171]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":89,"outFeatures":151}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":171,"outFeatures":151}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":302,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":85164}} {"id":"gen-grow-2579","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (60-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2579\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":798}},{"type":"update_params","name":"fc2","params":{"inFeatures":798,"outFeatures":798}},{"type":"update_params","name":"head","params":{"inFeatures":798,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":695856}} {"id":"gen-mlp-2580","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 328-feature input with 2 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2580\nComponents:\n - input (input) params={\"shape\":[1,328]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,328]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":328,"outFeatures":49}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":49,"outFeatures":969}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":969,"outFeatures":820}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":820,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":859773}} {"id":"gen-ae-2581","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 933-dim input: encode down to a 125-dim bottleneck and decode back to 933. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2581\nComponents:\n - input (input) params={\"shape\":[1,933]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,933]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":933,"outFeatures":1003}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1003,"outFeatures":125}},{"componentType":"linear","name":"dec1","params":{"inFeatures":125,"outFeatures":1003}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1003,"outFeatures":933}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2122348}} {"id":"gen-cnn-2582","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 69 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2582\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":45,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":49,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":47571}} {"id":"gen-txf-2583","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 442-token sequences with 23 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2583\nComponents:\n - input (input) params={\"shape\":[1,442]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,442]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18020,"embeddingDim":272}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":272,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":23}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":5499568}} {"id":"gen-gqa-2584","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 129-token sequences with 77 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2584\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,129]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27036,"embeddingDim":216}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":216,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[216]}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":5856408}} {"id":"gen-fix-2585","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (94) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2585\nComponents:\n - input (input) params={\"shape\":[1,478]}\n - embed (embedding) params={\"numEmbeddings\":48201,\"embeddingDim\":94}\n - attn (multiHeadAttention) params={\"embedDim\":94,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":94,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4567460}} {"id":"gen-trim-2586","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2831-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2586\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":2831}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2831,\"outFeatures\":2831}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2831,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":229}},{"type":"update_params","name":"fc2","params":{"inFeatures":229,"outFeatures":229}},{"type":"update_params","name":"head","params":{"inFeatures":229,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":85417}} {"id":"gen-norm-2587","family":"norm","seed":20260716,"spec":"This 105-feature, 54-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2587\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - fc1 (linear) params={\"inFeatures\":105,\"outFeatures\":176}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":176,\"outFeatures\":273}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":273,\"outFeatures\":65}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":65,\"outFeatures\":479}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":479,\"outFeatures\":149}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":149,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":176}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":273}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":65}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":479}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":149}}],"grade":{"pass":true,"score":100,"params":194825}} {"id":"gen-tower-2588","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 206-feature user input and a 80-feature item input, each through its own 2-layer MLP tower ending at width 424, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2588\nComponents:\n - user_input (input) params={\"shape\":[1,206]}\n - item_input (input) params={\"shape\":[1,80]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,206]}},{"componentType":"input","name":"item_input","params":{"shape":[1,80]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":206,"outFeatures":424}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":424,"outFeatures":424}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":80,"outFeatures":424}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":424,"outFeatures":424}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":848,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":481664}} {"id":"gen-grow-2589","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (43-feature input, 10 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2589\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - fc1 (linear) params={\"inFeatures\":43,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":10}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":43,"outFeatures":1109}},{"type":"update_params","name":"fc2","params":{"inFeatures":1109,"outFeatures":1109}},{"type":"update_params","name":"head","params":{"inFeatures":1109,"outFeatures":10}}],"grade":{"pass":true,"score":80,"params":1288658}} {"id":"gen-mlp-2590","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 57-feature input with 188 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2590\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,57]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":57,"outFeatures":272}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":272,"outFeatures":154}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":154,"outFeatures":793}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":793,"outFeatures":326}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":326,"outFeatures":220}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":220,"outFeatures":679}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":679,"outFeatures":188}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":786784}} {"id":"gen-ae-2591","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1864-dim input: encode down to a 113-dim bottleneck and decode back to 1864. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2591\nComponents:\n - input (input) params={\"shape\":[1,1864]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1864]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1864,"outFeatures":634}},{"componentType":"linear","name":"enc2","params":{"inFeatures":634,"outFeatures":113}},{"componentType":"linear","name":"dec1","params":{"inFeatures":113,"outFeatures":634}},{"componentType":"linear","name":"dec2","params":{"inFeatures":634,"outFeatures":1864}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2506836}} {"id":"gen-cnn-2592","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x53x53 image with 28 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2592\nComponents:\n - input (input) params={\"shape\":[1,3,53,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,53,53]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":64,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":83274}} {"id":"gen-txf-2593","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 116-token sequences with 60 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2593\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,116]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46494,"embeddingDim":288}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":288,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":288,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":13739328}} {"id":"gen-gqa-2594","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 279-token sequences with 77 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2594\nComponents:\n - input (input) params={\"shape\":[1,279]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,279]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37991,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":14618112}} {"id":"gen-fix-2595","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (362) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2595\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - embed (embedding) params={\"numEmbeddings\":34105,\"embeddingDim\":362}\n - attn (multiHeadAttention) params={\"embedDim\":362,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":362,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":12884304}} {"id":"gen-trim-2596","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3631-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2596\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - fc1 (linear) params={\"inFeatures\":123,\"outFeatures\":3631}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3631,\"outFeatures\":3631}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3631,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":123,"outFeatures":270}},{"type":"update_params","name":"fc2","params":{"inFeatures":270,"outFeatures":270}},{"type":"update_params","name":"head","params":{"inFeatures":270,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":107190}} {"id":"gen-norm-2597","family":"norm","seed":20260716,"spec":"This 162-feature, 26-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2597\nComponents:\n - input (input) params={\"shape\":[1,162]}\n - fc1 (linear) params={\"inFeatures\":162,\"outFeatures\":141}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":141,\"outFeatures\":69}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":69,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":141}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":69}}],"grade":{"pass":true,"score":94,"params":34365}} {"id":"gen-tower-2598","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 171-feature user input and a 111-feature item input, each through its own 2-layer MLP tower ending at width 365, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2598\nComponents:\n - user_input (input) params={\"shape\":[1,171]}\n - item_input (input) params={\"shape\":[1,111]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,171]}},{"componentType":"input","name":"item_input","params":{"shape":[1,111]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":171,"outFeatures":365}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":365,"outFeatures":365}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":111,"outFeatures":365}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":365,"outFeatures":365}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":730,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":370110}} {"id":"gen-grow-2599","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (105-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2599\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - fc1 (linear) params={\"inFeatures\":105,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":105,"outFeatures":659}},{"type":"update_params","name":"fc2","params":{"inFeatures":659,"outFeatures":659}},{"type":"update_params","name":"head","params":{"inFeatures":659,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":519292}} {"id":"gen-mlp-2600","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 257-feature input with 144 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2600\nComponents:\n - input (input) params={\"shape\":[1,257]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,257]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":257,"outFeatures":183}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":183,"outFeatures":668}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":668,"outFeatures":480}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":480,"outFeatures":441}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":441,"outFeatures":765}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":765,"outFeatures":144}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1149120}} {"id":"gen-ae-2601","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1368-dim input: encode down to a 181-dim bottleneck and decode back to 1368. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2601\nComponents:\n - input (input) params={\"shape\":[1,1368]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1368]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1368,"outFeatures":789}},{"componentType":"linear","name":"enc2","params":{"inFeatures":789,"outFeatures":181}},{"componentType":"linear","name":"dec1","params":{"inFeatures":181,"outFeatures":789}},{"componentType":"linear","name":"dec2","params":{"inFeatures":789,"outFeatures":1368}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2444322}} {"id":"gen-cnn-2602","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x40x40 image with 85 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2602\nComponents:\n - input (input) params={\"shape\":[1,3,40,40]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,40,40]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":13,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":59,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":58,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":51559}} {"id":"gen-txf-2603","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 284-token sequences with 71 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2603\nComponents:\n - input (input) params={\"shape\":[1,284]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,284]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32587,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":72,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2392848}} {"id":"gen-gqa-2604","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 374-token sequences with 5 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2604\nComponents:\n - input (input) params={\"shape\":[1,374]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,374]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17534,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":304,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[304]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":304,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":5}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5331856}} {"id":"gen-trim-2606","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3238-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2606\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":3238}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3238,\"outFeatures\":3238}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3238,\"outFeatures\":3238}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3238,\"outFeatures\":23}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":360}},{"type":"update_params","name":"fc2","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"fc3","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"head","params":{"inFeatures":360,"outFeatures":23}}],"grade":{"pass":true,"score":84,"params":285840}} {"id":"gen-norm-2607","family":"norm","seed":20260716,"spec":"This 98-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2607\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":421}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":421,\"outFeatures\":440}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":440,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":421}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":440}}],"grade":{"pass":true,"score":94,"params":259058}} {"id":"gen-tower-2608","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 53-feature user input and a 166-feature item input, each through its own 2-layer MLP tower ending at width 323, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2608\nComponents:\n - user_input (input) params={\"shape\":[1,53]}\n - item_input (input) params={\"shape\":[1,166]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,53]}},{"componentType":"input","name":"item_input","params":{"shape":[1,166]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":53,"outFeatures":323}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":323,"outFeatures":323}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":166,"outFeatures":323}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":323,"outFeatures":323}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":646,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":280041}} {"id":"gen-grow-2609","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (60-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2609\nComponents:\n - input (input) params={\"shape\":[1,60]}\n - fc1 (linear) params={\"inFeatures\":60,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":60,"outFeatures":954}},{"type":"update_params","name":"fc2","params":{"inFeatures":954,"outFeatures":954}},{"type":"update_params","name":"head","params":{"inFeatures":954,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":974988}} {"id":"gen-mlp-2610","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 251-feature input with 137 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2610\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,251]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":251,"outFeatures":296}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":296,"outFeatures":744}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":744,"outFeatures":437}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":437,"outFeatures":107}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":107,"outFeatures":137}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":681066}} {"id":"gen-ae-2611","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 258-dim input: encode down to a 52-dim bottleneck and decode back to 258. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2611\nComponents:\n - input (input) params={\"shape\":[1,258]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,258]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":258,"outFeatures":1016}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1016,"outFeatures":52}},{"componentType":"linear","name":"dec1","params":{"inFeatures":52,"outFeatures":1016}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1016,"outFeatures":258}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":629920}} {"id":"gen-cnn-2612","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x43x43 image with 41 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2612\nComponents:\n - input (input) params={\"shape\":[1,3,43,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,43,43]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":30,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":30,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":25,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":24,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30360}} {"id":"gen-txf-2613","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 93-token sequences with 58 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2613\nComponents:\n - input (input) params={\"shape\":[1,93]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,93]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33991,"embeddingDim":76}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":76,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2610828}} {"id":"gen-gqa-2614","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 480-token sequences with 86 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2614\nComponents:\n - input (input) params={\"shape\":[1,480]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,480]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34339,"embeddingDim":496}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":496,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[496]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":496,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[496]}},{"componentType":"linear","name":"head","params":{"inFeatures":496,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":17074800}} {"id":"gen-trim-2616","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3085-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2616\nComponents:\n - input (input) params={\"shape\":[1,139]}\n - fc1 (linear) params={\"inFeatures\":139,\"outFeatures\":3085}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3085,\"outFeatures\":3085}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3085,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":139,"outFeatures":188}},{"type":"update_params","name":"fc2","params":{"inFeatures":188,"outFeatures":188}},{"type":"update_params","name":"head","params":{"inFeatures":188,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":62980}} {"id":"gen-norm-2617","family":"norm","seed":20260716,"spec":"This 196-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2617\nComponents:\n - input (input) params={\"shape\":[1,196]}\n - fc1 (linear) params={\"inFeatures\":196,\"outFeatures\":261}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":261,\"outFeatures\":217}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":217,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":261}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":217}}],"grade":{"pass":true,"score":94,"params":124719}} {"id":"gen-tower-2618","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 173-feature user input and a 118-feature item input, each through its own 2-layer MLP tower ending at width 486, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2618\nComponents:\n - user_input (input) params={\"shape\":[1,173]}\n - item_input (input) params={\"shape\":[1,118]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,173]}},{"componentType":"input","name":"item_input","params":{"shape":[1,118]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":173,"outFeatures":486}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":486,"outFeatures":486}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":118,"outFeatures":486}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":486,"outFeatures":486}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":972,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":614790}} {"id":"gen-grow-2619","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (71-feature input, 12 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2619\nComponents:\n - input (input) params={\"shape\":[1,71]}\n - fc1 (linear) params={\"inFeatures\":71,\"outFeatures\":23}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":23,\"outFeatures\":23}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":23,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":71,"outFeatures":1156}},{"type":"update_params","name":"fc2","params":{"inFeatures":1156,"outFeatures":1156}},{"type":"update_params","name":"head","params":{"inFeatures":1156,"outFeatures":12}}],"grade":{"pass":true,"score":80,"params":1432284}} {"id":"gen-mlp-2620","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 228-feature input with 171 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2620\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":228,"outFeatures":43}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":43,"outFeatures":521}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":521,"outFeatures":321}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":321,"outFeatures":749}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":749,"outFeatures":171}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":567956}} {"id":"gen-ae-2621","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 849-dim input: encode down to a 149-dim bottleneck and decode back to 849. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2621\nComponents:\n - input (input) params={\"shape\":[1,849]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,849]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":849,"outFeatures":603}},{"componentType":"linear","name":"enc2","params":{"inFeatures":603,"outFeatures":149}},{"componentType":"linear","name":"dec1","params":{"inFeatures":149,"outFeatures":603}},{"componentType":"linear","name":"dec2","params":{"inFeatures":603,"outFeatures":849}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1203588}} {"id":"gen-cnn-2622","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 40 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2622\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":21,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":10,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":40}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":9674}} {"id":"gen-txf-2623","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 254-token sequences with 7 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2623\nComponents:\n - input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,254]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31435,"embeddingDim":152}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":152,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":152,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":152,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5056432}} {"id":"gen-gqa-2624","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 265-token sequences with 51 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2624\nComponents:\n - input (input) params={\"shape\":[1,265]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,265]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42658,"embeddingDim":144}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":144,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":144,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[144]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":144,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[144]}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6150096}} {"id":"gen-fix-2625","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (352) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2625\nComponents:\n - input (input) params={\"shape\":[1,242]}\n - embed (embedding) params={\"numEmbeddings\":48727,\"embeddingDim\":352}\n - attn (multiHeadAttention) params={\"embedDim\":352,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":352,\"outFeatures\":57}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":17667584}} {"id":"gen-trim-2626","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1716-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2626\nComponents:\n - input (input) params={\"shape\":[1,98]}\n - fc1 (linear) params={\"inFeatures\":98,\"outFeatures\":1716}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1716,\"outFeatures\":1716}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1716,\"outFeatures\":1716}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1716,\"outFeatures\":1716}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1716,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":98,"outFeatures":242}},{"type":"update_params","name":"fc2","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"fc3","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"fc4","params":{"inFeatures":242,"outFeatures":242}},{"type":"update_params","name":"head","params":{"inFeatures":242,"outFeatures":7}}],"grade":{"pass":true,"score":88,"params":201102}} {"id":"gen-norm-2627","family":"norm","seed":20260716,"spec":"This 61-feature, 97-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2627\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - fc1 (linear) params={\"inFeatures\":61,\"outFeatures\":388}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":388,\"outFeatures\":41}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":41,\"outFeatures\":211}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":211,\"outFeatures\":349}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":349,\"outFeatures\":97}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":388}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":41}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":211}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":349}}],"grade":{"pass":true,"score":100,"params":155719}} {"id":"gen-tower-2628","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 119-feature user input and a 87-feature item input, each through its own 2-layer MLP tower ending at width 153, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2628\nComponents:\n - user_input (input) params={\"shape\":[1,119]}\n - item_input (input) params={\"shape\":[1,87]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,119]}},{"componentType":"input","name":"item_input","params":{"shape":[1,87]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":119,"outFeatures":153}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":153,"outFeatures":153}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":87,"outFeatures":153}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":153,"outFeatures":153}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":306,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":78642}} {"id":"gen-grow-2629","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (106-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2629\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":18}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":18,\"outFeatures\":18}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":18,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":106,"outFeatures":1040}},{"type":"update_params","name":"fc2","params":{"inFeatures":1040,"outFeatures":1040}},{"type":"update_params","name":"head","params":{"inFeatures":1040,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":1200160}} {"id":"gen-mlp-2630","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 511-feature input with 172 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2630\nComponents:\n - input (input) params={\"shape\":[1,511]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,511]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":511,"outFeatures":957}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":957,"outFeatures":383}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":383,"outFeatures":295}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":295,"outFeatures":800}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":800,"outFeatures":618}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":618,"outFeatures":104}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":172}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1781103}} {"id":"gen-ae-2631","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 675-dim input: encode down to a 134-dim bottleneck and decode back to 675. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2631\nComponents:\n - input (input) params={\"shape\":[1,675]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,675]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":675,"outFeatures":721}},{"componentType":"linear","name":"enc2","params":{"inFeatures":721,"outFeatures":134}},{"componentType":"linear","name":"dec1","params":{"inFeatures":134,"outFeatures":721}},{"componentType":"linear","name":"dec2","params":{"inFeatures":721,"outFeatures":675}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1166578}} {"id":"gen-cnn-2632","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x41x41 image with 32 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2632\nComponents:\n - input (input) params={\"shape\":[1,3,41,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,41,41]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":25,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":25,"outChannels":63,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":63,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":40008}} {"id":"gen-txf-2633","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 449-token sequences with 99 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2633\nComponents:\n - input (input) params={\"shape\":[1,449]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,449]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29755,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":240,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7625760}} {"id":"gen-gqa-2634","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 321-token sequences with 54 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2634\nComponents:\n - input (input) params={\"shape\":[1,321]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,321]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21483,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":3445920}} {"id":"gen-trim-2636","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3247-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2636\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":3247}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3247,\"outFeatures\":3247}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3247,\"outFeatures\":3247}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3247,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":209}},{"type":"update_params","name":"fc2","params":{"inFeatures":209,"outFeatures":209}},{"type":"update_params","name":"fc3","params":{"inFeatures":209,"outFeatures":209}},{"type":"update_params","name":"head","params":{"inFeatures":209,"outFeatures":49}}],"grade":{"pass":true,"score":84,"params":119339}} {"id":"gen-norm-2637","family":"norm","seed":20260716,"spec":"This 113-feature, 72-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2637\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - fc1 (linear) params={\"inFeatures\":113,\"outFeatures\":443}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":443,\"outFeatures\":152}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":152,\"outFeatures\":89}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":89,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":443}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":152}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":89}}],"grade":{"pass":true,"score":100,"params":137331}} {"id":"gen-tower-2638","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 142-feature user input and a 67-feature item input, each through its own 2-layer MLP tower ending at width 465, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2638\nComponents:\n - user_input (input) params={\"shape\":[1,142]}\n - item_input (input) params={\"shape\":[1,67]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,142]}},{"componentType":"input","name":"item_input","params":{"shape":[1,67]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":142,"outFeatures":465}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":465,"outFeatures":465}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":67,"outFeatures":465}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":465,"outFeatures":465}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":930,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":530565}} {"id":"gen-grow-2639","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (106-feature input, 28 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2639\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":28}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":106,"outFeatures":669}},{"type":"update_params","name":"fc2","params":{"inFeatures":669,"outFeatures":669}},{"type":"update_params","name":"head","params":{"inFeatures":669,"outFeatures":28}}],"grade":{"pass":true,"score":80,"params":537207}} {"id":"gen-mlp-2640","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 346-feature input with 170 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2640\nComponents:\n - input (input) params={\"shape\":[1,346]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,346]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":346,"outFeatures":839}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":839,"outFeatures":927}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":927,"outFeatures":200}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":200,"outFeatures":170}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1287447}} {"id":"gen-ae-2641","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 668-dim input: encode down to a 178-dim bottleneck and decode back to 668. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2641\nComponents:\n - input (input) params={\"shape\":[1,668]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,668]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":668,"outFeatures":981}},{"componentType":"linear","name":"enc2","params":{"inFeatures":981,"outFeatures":178}},{"componentType":"linear","name":"dec1","params":{"inFeatures":178,"outFeatures":981}},{"componentType":"linear","name":"dec2","params":{"inFeatures":981,"outFeatures":668}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1659852}} {"id":"gen-cnn-2642","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x45x45 image with 61 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2642\nComponents:\n - input (input) params={\"shape\":[1,3,45,45]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,45,45]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":19,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":44,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":33,"outFeatures":61}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":46212}} {"id":"gen-txf-2643","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 219-token sequences with 32 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2643\nComponents:\n - input (input) params={\"shape\":[1,219]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,219]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39910,"embeddingDim":36}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":36,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":36,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":36,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1448280}} {"id":"gen-gqa-2644","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 335-token sequences with 13 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2644\nComponents:\n - input (input) params={\"shape\":[1,335]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,335]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9196,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":1473440}} {"id":"gen-fix-2645","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (398) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2645\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - embed (embedding) params={\"numEmbeddings\":42753,\"embeddingDim\":398}\n - attn (multiHeadAttention) params={\"embedDim\":398,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":398,\"outFeatures\":60}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":17673190}} {"id":"gen-trim-2646","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1719-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2646\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":1719}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1719,\"outFeatures\":1719}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1719,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":53,"outFeatures":213}},{"type":"update_params","name":"fc2","params":{"inFeatures":213,"outFeatures":213}},{"type":"update_params","name":"head","params":{"inFeatures":213,"outFeatures":40}}],"grade":{"pass":true,"score":80,"params":65178}} {"id":"gen-norm-2647","family":"norm","seed":20260716,"spec":"This 154-feature, 88-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2647\nComponents:\n - input (input) params={\"shape\":[1,154]}\n - fc1 (linear) params={\"inFeatures\":154,\"outFeatures\":45}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":45,\"outFeatures\":150}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":150,\"outFeatures\":52}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":52,\"outFeatures\":61}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":61,\"outFeatures\":384}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":384,\"outFeatures\":88}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":45}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":150}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":52}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":61}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":384}}],"grade":{"pass":true,"score":100,"params":81868}} {"id":"gen-tower-2648","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 138-feature user input and a 44-feature item input, each through its own 2-layer MLP tower ending at width 193, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2648\nComponents:\n - user_input (input) params={\"shape\":[1,138]}\n - item_input (input) params={\"shape\":[1,44]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,138]}},{"componentType":"input","name":"item_input","params":{"shape":[1,44]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":138,"outFeatures":193}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":193,"outFeatures":193}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":44,"outFeatures":193}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":193,"outFeatures":193}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":386,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":110010}} {"id":"gen-grow-2649","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (115-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2649\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":1037}},{"type":"update_params","name":"fc2","params":{"inFeatures":1037,"outFeatures":1037}},{"type":"update_params","name":"head","params":{"inFeatures":1037,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":1237141}} {"id":"gen-mlp-2650","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 443-feature input with 105 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2650\nComponents:\n - input (input) params={\"shape\":[1,443]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,443]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":443,"outFeatures":912}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":912,"outFeatures":30}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":30,"outFeatures":105}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":434526}} {"id":"gen-ae-2651","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1161-dim input: encode down to a 114-dim bottleneck and decode back to 1161. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2651\nComponents:\n - input (input) params={\"shape\":[1,1161]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1161]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1161,"outFeatures":464}},{"componentType":"linear","name":"enc2","params":{"inFeatures":464,"outFeatures":114}},{"componentType":"linear","name":"dec1","params":{"inFeatures":114,"outFeatures":464}},{"componentType":"linear","name":"dec2","params":{"inFeatures":464,"outFeatures":1161}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1183200}} {"id":"gen-cnn-2652","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 62 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2652\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":43,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":43,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":52,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":55,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":64292}} {"id":"gen-txf-2653","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 151-token sequences with 80 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2653\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,151]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48968,"embeddingDim":496}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":496,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":496,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":496,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":496,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":27280000}} {"id":"gen-gqa-2654","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 391-token sequences with 19 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2654\nComponents:\n - input (input) params={\"shape\":[1,391]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,391]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33284,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":19}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4529208}} {"id":"gen-trim-2656","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2142-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2656\nComponents:\n - input (input) params={\"shape\":[1,164]}\n - fc1 (linear) params={\"inFeatures\":164,\"outFeatures\":2142}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2142,\"outFeatures\":2142}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2142,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":164,"outFeatures":190}},{"type":"update_params","name":"fc2","params":{"inFeatures":190,"outFeatures":190}},{"type":"update_params","name":"head","params":{"inFeatures":190,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":67640}} {"id":"gen-norm-2657","family":"norm","seed":20260716,"spec":"This 218-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2657\nComponents:\n - input (input) params={\"shape\":[1,218]}\n - fc1 (linear) params={\"inFeatures\":218,\"outFeatures\":460}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":460,\"outFeatures\":56}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":56,\"outFeatures\":260}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":260,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":460}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":56}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":260}}],"grade":{"pass":true,"score":100,"params":154380}} {"id":"gen-tower-2658","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 58-feature user input and a 247-feature item input, each through its own 2-layer MLP tower ending at width 410, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2658\nComponents:\n - user_input (input) params={\"shape\":[1,58]}\n - item_input (input) params={\"shape\":[1,247]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,58]}},{"componentType":"input","name":"item_input","params":{"shape":[1,247]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":58,"outFeatures":410}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":410,"outFeatures":410}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":247,"outFeatures":410}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":410,"outFeatures":410}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":820,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":462070}} {"id":"gen-grow-2659","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2659\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":791}},{"type":"update_params","name":"fc2","params":{"inFeatures":791,"outFeatures":791}},{"type":"update_params","name":"head","params":{"inFeatures":791,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":706363}} {"id":"gen-mlp-2660","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 301-feature input with 180 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2660\nComponents:\n - input (input) params={\"shape\":[1,301]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,301]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":301,"outFeatures":623}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":623,"outFeatures":858}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":858,"outFeatures":318}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":318,"outFeatures":52}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":180}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1020797}} {"id":"gen-ae-2661","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1195-dim input: encode down to a 13-dim bottleneck and decode back to 1195. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2661\nComponents:\n - input (input) params={\"shape\":[1,1195]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1195]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1195,"outFeatures":420}},{"componentType":"linear","name":"enc2","params":{"inFeatures":420,"outFeatures":13}},{"componentType":"linear","name":"dec1","params":{"inFeatures":13,"outFeatures":420}},{"componentType":"linear","name":"dec2","params":{"inFeatures":420,"outFeatures":1195}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1014720}} {"id":"gen-cnn-2662","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 75 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2662\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":37,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":58,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":26,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":46,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":48099}} {"id":"gen-txf-2663","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 358-token sequences with 31 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2663\nComponents:\n - input (input) params={\"shape\":[1,358]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,358]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9093,"embeddingDim":32}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":32,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":32,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":300160}} {"id":"gen-gqa-2664","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 222-token sequences with 42 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2664\nComponents:\n - input (input) params={\"shape\":[1,222]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,222]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18560,"embeddingDim":272}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":272,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[272]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":272,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[272]}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5059744}} {"id":"gen-trim-2666","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2852-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2666\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - fc1 (linear) params={\"inFeatures\":192,\"outFeatures\":2852}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2852,\"outFeatures\":2852}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2852,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":192,"outFeatures":261}},{"type":"update_params","name":"fc2","params":{"inFeatures":261,"outFeatures":261}},{"type":"update_params","name":"head","params":{"inFeatures":261,"outFeatures":34}}],"grade":{"pass":true,"score":80,"params":127107}} {"id":"gen-norm-2667","family":"norm","seed":20260716,"spec":"This 124-feature, 92-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2667\nComponents:\n - input (input) params={\"shape\":[1,124]}\n - fc1 (linear) params={\"inFeatures\":124,\"outFeatures\":454}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":454,\"outFeatures\":394}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":394,\"outFeatures\":125}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":125,\"outFeatures\":435}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":435,\"outFeatures\":366}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":366,\"outFeatures\":92}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":454}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":394}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":125}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":435}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":366}}],"grade":{"pass":true,"score":100,"params":531679}} {"id":"gen-tower-2668","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 149-feature user input and a 156-feature item input, each through its own 2-layer MLP tower ending at width 328, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2668\nComponents:\n - user_input (input) params={\"shape\":[1,149]}\n - item_input (input) params={\"shape\":[1,156]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,149]}},{"componentType":"input","name":"item_input","params":{"shape":[1,156]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":149,"outFeatures":328}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":328,"outFeatures":328}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":156,"outFeatures":328}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":328,"outFeatures":328}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":315864}} {"id":"gen-grow-2669","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (30-feature input, 45 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2669\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":30,"outFeatures":830}},{"type":"update_params","name":"fc2","params":{"inFeatures":830,"outFeatures":830}},{"type":"update_params","name":"head","params":{"inFeatures":830,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":751150}} {"id":"gen-mlp-2670","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 274-feature input with 57 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2670\nComponents:\n - input (input) params={\"shape\":[1,274]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,274]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":274,"outFeatures":999}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":999,"outFeatures":175}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":175,"outFeatures":64}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":64,"outFeatures":469}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":469,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":516500}} {"id":"gen-ae-2671","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 151-dim input: encode down to a 215-dim bottleneck and decode back to 151. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2671\nComponents:\n - input (input) params={\"shape\":[1,151]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,151]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":151,"outFeatures":918}},{"componentType":"linear","name":"enc2","params":{"inFeatures":918,"outFeatures":215}},{"componentType":"linear","name":"dec1","params":{"inFeatures":215,"outFeatures":918}},{"componentType":"linear","name":"dec2","params":{"inFeatures":918,"outFeatures":151}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":671976}} {"id":"gen-cnn-2672","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 46 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2672\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":52,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":39,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":15483}} {"id":"gen-txf-2673","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 372-token sequences with 85 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2673\nComponents:\n - input (input) params={\"shape\":[1,372]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,372]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15746,"embeddingDim":40}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":40,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":40,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":646040}} {"id":"gen-gqa-2674","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 282-token sequences with 64 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2674\nComponents:\n - input (input) params={\"shape\":[1,282]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,282]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10276,"embeddingDim":164}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[164]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[164]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":164,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[164]}},{"componentType":"linear","name":"head","params":{"inFeatures":164,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1695760}} {"id":"gen-trim-2676","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 4043-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2676\nComponents:\n - input (input) params={\"shape\":[1,186]}\n - fc1 (linear) params={\"inFeatures\":186,\"outFeatures\":4043}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4043,\"outFeatures\":4043}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4043,\"outFeatures\":4043}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":4043,\"outFeatures\":4043}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":4043,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":186,"outFeatures":319}},{"type":"update_params","name":"fc2","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"fc3","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"fc4","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"head","params":{"inFeatures":319,"outFeatures":36}}],"grade":{"pass":true,"score":88,"params":376101}} {"id":"gen-norm-2677","family":"norm","seed":20260716,"spec":"This 47-feature, 72-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2677\nComponents:\n - input (input) params={\"shape\":[1,47]}\n - fc1 (linear) params={\"inFeatures\":47,\"outFeatures\":323}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":323,\"outFeatures\":147}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":147,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":323}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":147}}],"grade":{"pass":true,"score":94,"params":73246}} {"id":"gen-tower-2678","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 41-feature user input and a 88-feature item input, each through its own 2-layer MLP tower ending at width 174, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2678\nComponents:\n - user_input (input) params={\"shape\":[1,41]}\n - item_input (input) params={\"shape\":[1,88]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,41]}},{"componentType":"input","name":"item_input","params":{"shape":[1,88]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":41,"outFeatures":174}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":174,"outFeatures":174}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":88,"outFeatures":174}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":174,"outFeatures":174}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":348,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":83346}} {"id":"gen-grow-2679","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (115-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2679\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":962}},{"type":"update_params","name":"fc2","params":{"inFeatures":962,"outFeatures":962}},{"type":"update_params","name":"head","params":{"inFeatures":962,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":1055314}} {"id":"gen-mlp-2680","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 488-feature input with 28 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2680\nComponents:\n - input (input) params={\"shape\":[1,488]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,488]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":488,"outFeatures":930}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":930,"outFeatures":418}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":418,"outFeatures":67}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":67,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":872462}} {"id":"gen-ae-2681","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 495-dim input: encode down to a 109-dim bottleneck and decode back to 495. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2681\nComponents:\n - input (input) params={\"shape\":[1,495]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,495]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":495,"outFeatures":610}},{"componentType":"linear","name":"enc2","params":{"inFeatures":610,"outFeatures":109}},{"componentType":"linear","name":"dec1","params":{"inFeatures":109,"outFeatures":610}},{"componentType":"linear","name":"dec2","params":{"inFeatures":610,"outFeatures":495}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":736880}} {"id":"gen-cnn-2682","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 91 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2682\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":21,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":51,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":56,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":57287}} {"id":"gen-txf-2683","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 288-token sequences with 16 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2683\nComponents:\n - input (input) params={\"shape\":[1,288]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,288]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41958,"embeddingDim":344}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":344,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":344,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":344,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":15859088}} {"id":"gen-gqa-2684","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 89-token sequences with 71 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2684\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,89]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41641,"embeddingDim":88}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":88,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[88]}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":71}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3670656}} {"id":"gen-trim-2686","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3486-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2686\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":3486}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3486,\"outFeatures\":3486}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3486,\"outFeatures\":3486}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3486,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":115,"outFeatures":332}},{"type":"update_params","name":"fc2","params":{"inFeatures":332,"outFeatures":332}},{"type":"update_params","name":"fc3","params":{"inFeatures":332,"outFeatures":332}},{"type":"update_params","name":"head","params":{"inFeatures":332,"outFeatures":31}}],"grade":{"pass":true,"score":84,"params":268920}} {"id":"gen-norm-2687","family":"norm","seed":20260716,"spec":"This 192-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2687\nComponents:\n - input (input) params={\"shape\":[1,192]}\n - fc1 (linear) params={\"inFeatures\":192,\"outFeatures\":298}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":298,\"outFeatures\":123}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":123,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":298}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":123}}],"grade":{"pass":true,"score":94,"params":100389}} {"id":"gen-tower-2688","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 118-feature user input and a 240-feature item input, each through its own 2-layer MLP tower ending at width 259, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2688\nComponents:\n - user_input (input) params={\"shape\":[1,118]}\n - item_input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,118]}},{"componentType":"input","name":"item_input","params":{"shape":[1,240]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":118,"outFeatures":259}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":259,"outFeatures":259}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":240,"outFeatures":259}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":259,"outFeatures":259}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":518,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":227402}} {"id":"gen-grow-2689","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (76-feature input, 45 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2689\nComponents:\n - input (input) params={\"shape\":[1,76]}\n - fc1 (linear) params={\"inFeatures\":76,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":76,"outFeatures":664}},{"type":"update_params","name":"fc2","params":{"inFeatures":664,"outFeatures":664}},{"type":"update_params","name":"head","params":{"inFeatures":664,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":521240}} {"id":"gen-mlp-2690","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 36-feature input with 26 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2690\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,36]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":36,"outFeatures":363}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":363,"outFeatures":276}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":276,"outFeatures":34}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":34,"outFeatures":270}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":270,"outFeatures":751}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":751,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":354116}} {"id":"gen-ae-2691","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 819-dim input: encode down to a 114-dim bottleneck and decode back to 819. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2691\nComponents:\n - input (input) params={\"shape\":[1,819]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,819]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":819,"outFeatures":554}},{"componentType":"linear","name":"enc2","params":{"inFeatures":554,"outFeatures":114}},{"componentType":"linear","name":"dec1","params":{"inFeatures":114,"outFeatures":554}},{"componentType":"linear","name":"dec2","params":{"inFeatures":554,"outFeatures":819}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1033764}} {"id":"gen-cnn-2692","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x27x27 image with 47 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2692\nComponents:\n - input (input) params={\"shape\":[1,3,27,27]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,27,27]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":38,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":21343}} {"id":"gen-txf-2693","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 356-token sequences with 70 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2693\nComponents:\n - input (input) params={\"shape\":[1,356]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,356]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":11917,"embeddingDim":272}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":272,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":272,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":272,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4148272}} {"id":"gen-gqa-2694","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 496-token sequences with 100 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2694\nComponents:\n - input (input) params={\"shape\":[1,496]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,496]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41162,"embeddingDim":416}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":416,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[416]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":416,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[416]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":416,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[416]}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":17164992}} {"id":"gen-trim-2696","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2103-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2696\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":2103}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2103,\"outFeatures\":2103}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2103,\"outFeatures\":2103}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2103,\"outFeatures\":2103}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2103,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":231}},{"type":"update_params","name":"fc2","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"fc3","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"fc4","params":{"inFeatures":231,"outFeatures":231}},{"type":"update_params","name":"head","params":{"inFeatures":231,"outFeatures":42}}],"grade":{"pass":true,"score":88,"params":189882}} {"id":"gen-norm-2697","family":"norm","seed":20260716,"spec":"This 38-feature, 93-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2697\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":159}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":159,\"outFeatures\":409}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":409,\"outFeatures\":93}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":159}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":409}}],"grade":{"pass":true,"score":94,"params":109110}} {"id":"gen-tower-2698","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 240-feature user input and a 26-feature item input, each through its own 2-layer MLP tower ending at width 252, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2698\nComponents:\n - user_input (input) params={\"shape\":[1,240]}\n - item_input (input) params={\"shape\":[1,26]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,240]}},{"componentType":"input","name":"item_input","params":{"shape":[1,26]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":240,"outFeatures":252}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":252,"outFeatures":252}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":26,"outFeatures":252}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":252,"outFeatures":252}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":504,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":194544}} {"id":"gen-grow-2699","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (30-feature input, 3 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2699\nComponents:\n - input (input) params={\"shape\":[1,30]}\n - fc1 (linear) params={\"inFeatures\":30,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":3}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":30,"outFeatures":1198}},{"type":"update_params","name":"fc2","params":{"inFeatures":1198,"outFeatures":1198}},{"type":"update_params","name":"head","params":{"inFeatures":1198,"outFeatures":3}}],"grade":{"pass":true,"score":80,"params":1474738}} {"id":"gen-mlp-2700","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 132-feature input with 31 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2700\nComponents:\n - input (input) params={\"shape\":[1,132]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,132]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":132,"outFeatures":909}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":909,"outFeatures":517}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":517,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":605968}} {"id":"gen-ae-2701","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 362-dim input: encode down to a 7-dim bottleneck and decode back to 362. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2701\nComponents:\n - input (input) params={\"shape\":[1,362]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,362]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":362,"outFeatures":682}},{"componentType":"linear","name":"enc2","params":{"inFeatures":682,"outFeatures":7}},{"componentType":"linear","name":"dec1","params":{"inFeatures":7,"outFeatures":682}},{"componentType":"linear","name":"dec2","params":{"inFeatures":682,"outFeatures":362}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":503316}} {"id":"gen-cnn-2702","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 38 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2702\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":6581}} {"id":"gen-txf-2703","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 294-token sequences with 15 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2703\nComponents:\n - input (input) params={\"shape\":[1,294]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,294]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33284,"embeddingDim":220}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":220,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":220,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":220,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":220,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":7906580}} {"id":"gen-gqa-2704","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 390-token sequences with 93 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2704\nComponents:\n - input (input) params={\"shape\":[1,390]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,390]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33715,"embeddingDim":68}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":68,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[68]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":68,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[68]}},{"componentType":"linear","name":"head","params":{"inFeatures":68,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2298944}} {"id":"gen-fix-2705","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (324) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2705\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - embed (embedding) params={\"numEmbeddings\":23374,\"embeddingDim\":324}\n - attn (multiHeadAttention) params={\"embedDim\":324,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":324,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":7994376}} {"id":"gen-trim-2706","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3617-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2706\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - fc1 (linear) params={\"inFeatures\":58,\"outFeatures\":3617}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3617,\"outFeatures\":3617}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3617,\"outFeatures\":3617}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3617,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":58,"outFeatures":110}},{"type":"update_params","name":"fc2","params":{"inFeatures":110,"outFeatures":110}},{"type":"update_params","name":"fc3","params":{"inFeatures":110,"outFeatures":110}},{"type":"update_params","name":"head","params":{"inFeatures":110,"outFeatures":4}}],"grade":{"pass":true,"score":84,"params":31020}} {"id":"gen-norm-2707","family":"norm","seed":20260716,"spec":"This 106-feature, 34-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2707\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":344}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":344,\"outFeatures\":175}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":175,\"outFeatures\":163}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":163,\"outFeatures\":270}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":270,\"outFeatures\":36}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":36,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":344}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":175}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":163}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":270}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":36}}],"grade":{"pass":true,"score":100,"params":180143}} {"id":"gen-tower-2708","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 89-feature user input and a 60-feature item input, each through its own 2-layer MLP tower ending at width 466, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2708\nComponents:\n - user_input (input) params={\"shape\":[1,89]}\n - item_input (input) params={\"shape\":[1,60]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,89]}},{"componentType":"input","name":"item_input","params":{"shape":[1,60]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":89,"outFeatures":466}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":60,"outFeatures":466}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":932,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":504678}} {"id":"gen-grow-2709","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (67-feature input, 22 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2709\nComponents:\n - input (input) params={\"shape\":[1,67]}\n - fc1 (linear) params={\"inFeatures\":67,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":67,"outFeatures":1104}},{"type":"update_params","name":"fc2","params":{"inFeatures":1104,"outFeatures":1104}},{"type":"update_params","name":"head","params":{"inFeatures":1104,"outFeatures":22}}],"grade":{"pass":true,"score":80,"params":1317072}} {"id":"gen-mlp-2710","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 353-feature input with 130 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2710\nComponents:\n - input (input) params={\"shape\":[1,353]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,353]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":353,"outFeatures":202}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":202,"outFeatures":191}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":191,"outFeatures":179}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":179,"outFeatures":130}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":167347}} {"id":"gen-ae-2711","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 273-dim input: encode down to a 222-dim bottleneck and decode back to 273. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2711\nComponents:\n - input (input) params={\"shape\":[1,273]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,273]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":273,"outFeatures":660}},{"componentType":"linear","name":"enc2","params":{"inFeatures":660,"outFeatures":222}},{"componentType":"linear","name":"dec1","params":{"inFeatures":222,"outFeatures":660}},{"componentType":"linear","name":"dec2","params":{"inFeatures":660,"outFeatures":273}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":653400}} {"id":"gen-cnn-2712","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 88 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2712\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":37,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":37,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":8,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":10864}} {"id":"gen-txf-2713","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 58-token sequences with 100 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2713\nComponents:\n - input (input) params={\"shape\":[1,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,58]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45573,"embeddingDim":152}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":152,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":152,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":100}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7127128}} {"id":"gen-gqa-2714","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 432-token sequences with 74 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2714\nComponents:\n - input (input) params={\"shape\":[1,432]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,432]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34509,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":224,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7746592}} {"id":"gen-trim-2716","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3218-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2716\nComponents:\n - input (input) params={\"shape\":[1,52]}\n - fc1 (linear) params={\"inFeatures\":52,\"outFeatures\":3218}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3218,\"outFeatures\":3218}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3218,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":52,"outFeatures":92}},{"type":"update_params","name":"fc2","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"head","params":{"inFeatures":92,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":17112}} {"id":"gen-norm-2717","family":"norm","seed":20260716,"spec":"This 194-feature, 79-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2717\nComponents:\n - input (input) params={\"shape\":[1,194]}\n - fc1 (linear) params={\"inFeatures\":194,\"outFeatures\":39}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":39,\"outFeatures\":246}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":246,\"outFeatures\":79}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":39}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":246}}],"grade":{"pass":true,"score":94,"params":36594}} {"id":"gen-tower-2718","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 221-feature user input and a 204-feature item input, each through its own 2-layer MLP tower ending at width 466, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2718\nComponents:\n - user_input (input) params={\"shape\":[1,221]}\n - item_input (input) params={\"shape\":[1,204]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,221]}},{"componentType":"input","name":"item_input","params":{"shape":[1,204]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":221,"outFeatures":466}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":204,"outFeatures":466}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":466,"outFeatures":466}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":932,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":633294}} {"id":"gen-grow-2719","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (121-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2719\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - fc1 (linear) params={\"inFeatures\":121,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":121,"outFeatures":843}},{"type":"update_params","name":"fc2","params":{"inFeatures":843,"outFeatures":843}},{"type":"update_params","name":"head","params":{"inFeatures":843,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":827826}} {"id":"gen-mlp-2720","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 444-feature input with 3 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2720\nComponents:\n - input (input) params={\"shape\":[1,444]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,444]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":444,"outFeatures":776}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":776,"outFeatures":376}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":376,"outFeatures":888}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":888,"outFeatures":94}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":94,"outFeatures":91}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":91,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1062507}} {"id":"gen-ae-2721","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1305-dim input: encode down to a 45-dim bottleneck and decode back to 1305. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2721\nComponents:\n - input (input) params={\"shape\":[1,1305]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1305]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1305,"outFeatures":143}},{"componentType":"linear","name":"enc2","params":{"inFeatures":143,"outFeatures":45}},{"componentType":"linear","name":"dec1","params":{"inFeatures":45,"outFeatures":143}},{"componentType":"linear","name":"dec2","params":{"inFeatures":143,"outFeatures":1305}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":386100}} {"id":"gen-cnn-2722","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 86 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2722\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":34,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":34,"outFeatures":86}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":22238}} {"id":"gen-txf-2723","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 135-token sequences with 97 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2723\nComponents:\n - input (input) params={\"shape\":[1,135]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,135]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":36714,"embeddingDim":244}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":244,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":244,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":244,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":9458172}} {"id":"gen-gqa-2724","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 156-token sequences with 9 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2724\nComponents:\n - input (input) params={\"shape\":[1,156]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,156]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38677,"embeddingDim":172}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[172]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[172]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[172]}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6653992}} {"id":"gen-fix-2725","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (378) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2725\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - embed (embedding) params={\"numEmbeddings\":17734,\"embeddingDim\":378}\n - attn (multiHeadAttention) params={\"embedDim\":378,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":378,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":7304472}} {"id":"gen-trim-2726","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1962-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2726\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":1962}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1962,\"outFeatures\":1962}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1962,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":364}},{"type":"update_params","name":"fc2","params":{"inFeatures":364,"outFeatures":364}},{"type":"update_params","name":"head","params":{"inFeatures":364,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":180180}} {"id":"gen-norm-2727","family":"norm","seed":20260716,"spec":"This 57-feature, 55-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2727\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - fc1 (linear) params={\"inFeatures\":57,\"outFeatures\":448}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":448,\"outFeatures\":503}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":503,\"outFeatures\":455}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":455,\"outFeatures\":326}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":326,\"outFeatures\":504}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":504,\"outFeatures\":55}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":448}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":503}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":455}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":326}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":504}}],"grade":{"pass":true,"score":100,"params":820099}} {"id":"gen-tower-2728","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 208-feature user input and a 53-feature item input, each through its own 2-layer MLP tower ending at width 478, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2728\nComponents:\n - user_input (input) params={\"shape\":[1,208]}\n - item_input (input) params={\"shape\":[1,53]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,208]}},{"componentType":"input","name":"item_input","params":{"shape":[1,53]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":208,"outFeatures":478}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":53,"outFeatures":478}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":478,"outFeatures":478}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":956,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":582682}} {"id":"gen-grow-2729","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (21-feature input, 27 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2729\nComponents:\n - input (input) params={\"shape\":[1,21]}\n - fc1 (linear) params={\"inFeatures\":21,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":21,"outFeatures":1051}},{"type":"update_params","name":"fc2","params":{"inFeatures":1051,"outFeatures":1051}},{"type":"update_params","name":"head","params":{"inFeatures":1051,"outFeatures":27}}],"grade":{"pass":true,"score":80,"params":1155049}} {"id":"gen-mlp-2730","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 23-feature input with 146 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2730\nComponents:\n - input (input) params={\"shape\":[1,23]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,23]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":23,"outFeatures":80}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":80,"outFeatures":705}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":705,"outFeatures":270}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":270,"outFeatures":776}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":776,"outFeatures":146}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":571406}} {"id":"gen-ae-2731","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1941-dim input: encode down to a 219-dim bottleneck and decode back to 1941. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2731\nComponents:\n - input (input) params={\"shape\":[1,1941]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1941]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1941,"outFeatures":378}},{"componentType":"linear","name":"enc2","params":{"inFeatures":378,"outFeatures":219}},{"componentType":"linear","name":"dec1","params":{"inFeatures":219,"outFeatures":378}},{"componentType":"linear","name":"dec2","params":{"inFeatures":378,"outFeatures":1941}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1632960}} {"id":"gen-cnn-2732","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x46x46 image with 3 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2732\nComponents:\n - input (input) params={\"shape\":[1,3,46,46]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,46,46]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":44,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":20,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9168}} {"id":"gen-txf-2733","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 43-token sequences with 54 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2733\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,43]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22244,"embeddingDim":74}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":74,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":74,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1671956}} {"id":"gen-gqa-2734","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 390-token sequences with 3 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2734\nComponents:\n - input (input) params={\"shape\":[1,390]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,390]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34581,"embeddingDim":160}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":160,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":160,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[160]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":160,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[160]}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":5533440}} {"id":"gen-trim-2736","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3194-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2736\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - fc1 (linear) params={\"inFeatures\":225,\"outFeatures\":3194}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3194,\"outFeatures\":3194}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3194,\"outFeatures\":3194}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3194,\"outFeatures\":3194}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3194,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":225,"outFeatures":311}},{"type":"update_params","name":"fc2","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"fc3","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"fc4","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"head","params":{"inFeatures":311,"outFeatures":11}}],"grade":{"pass":true,"score":88,"params":363559}} {"id":"gen-norm-2737","family":"norm","seed":20260716,"spec":"This 185-feature, 45-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2737\nComponents:\n - input (input) params={\"shape\":[1,185]}\n - fc1 (linear) params={\"inFeatures\":185,\"outFeatures\":289}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":289,\"outFeatures\":249}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":249,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":289}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":249}}],"grade":{"pass":true,"score":94,"params":136631}} {"id":"gen-tower-2738","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 38-feature user input and a 178-feature item input, each through its own 2-layer MLP tower ending at width 42, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2738\nComponents:\n - user_input (input) params={\"shape\":[1,38]}\n - item_input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,38]}},{"componentType":"input","name":"item_input","params":{"shape":[1,178]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":38,"outFeatures":42}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":42,"outFeatures":42}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":178,"outFeatures":42}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":42,"outFeatures":42}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":12684}} {"id":"gen-grow-2739","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (87-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2739\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - fc1 (linear) params={\"inFeatures\":87,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":87,"outFeatures":944}},{"type":"update_params","name":"fc2","params":{"inFeatures":944,"outFeatures":944}},{"type":"update_params","name":"head","params":{"inFeatures":944,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":997808}} {"id":"gen-mlp-2740","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 451-feature input with 174 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2740\nComponents:\n - input (input) params={\"shape\":[1,451]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,451]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":451,"outFeatures":694}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":694,"outFeatures":193}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":193,"outFeatures":375}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":375,"outFeatures":48}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":174}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":545663}} {"id":"gen-ae-2741","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 842-dim input: encode down to a 124-dim bottleneck and decode back to 842. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2741\nComponents:\n - input (input) params={\"shape\":[1,842]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,842]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":842,"outFeatures":561}},{"componentType":"linear","name":"enc2","params":{"inFeatures":561,"outFeatures":124}},{"componentType":"linear","name":"dec1","params":{"inFeatures":124,"outFeatures":561}},{"componentType":"linear","name":"dec2","params":{"inFeatures":561,"outFeatures":842}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1083852}} {"id":"gen-cnn-2742","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 55 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2742\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":14,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9950}} {"id":"gen-txf-2743","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 440-token sequences with 96 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2743\nComponents:\n - input (input) params={\"shape\":[1,440]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,440]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32243,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":240,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":240,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":8452560}} {"id":"gen-gqa-2744","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 423-token sequences with 37 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2744\nComponents:\n - input (input) params={\"shape\":[1,423]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,423]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10618,"embeddingDim":344}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":344,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[344]}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3665320}} {"id":"gen-trim-2746","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3116-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2746\nComponents:\n - input (input) params={\"shape\":[1,152]}\n - fc1 (linear) params={\"inFeatures\":152,\"outFeatures\":3116}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3116,\"outFeatures\":3116}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3116,\"outFeatures\":3116}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3116,\"outFeatures\":3116}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3116,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":152,"outFeatures":226}},{"type":"update_params","name":"fc2","params":{"inFeatures":226,"outFeatures":226}},{"type":"update_params","name":"fc3","params":{"inFeatures":226,"outFeatures":226}},{"type":"update_params","name":"fc4","params":{"inFeatures":226,"outFeatures":226}},{"type":"update_params","name":"head","params":{"inFeatures":226,"outFeatures":42}}],"grade":{"pass":true,"score":88,"params":197072}} {"id":"gen-norm-2747","family":"norm","seed":20260716,"spec":"This 73-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2747\nComponents:\n - input (input) params={\"shape\":[1,73]}\n - fc1 (linear) params={\"inFeatures\":73,\"outFeatures\":494}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":494,\"outFeatures\":508}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":508,\"outFeatures\":196}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":196,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":494}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":508}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":196}}],"grade":{"pass":true,"score":100,"params":399714}} {"id":"gen-tower-2748","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 111-feature user input and a 172-feature item input, each through its own 2-layer MLP tower ending at width 55, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2748\nComponents:\n - user_input (input) params={\"shape\":[1,111]}\n - item_input (input) params={\"shape\":[1,172]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,111]}},{"componentType":"input","name":"item_input","params":{"shape":[1,172]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":111,"outFeatures":55}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":55,"outFeatures":55}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":172,"outFeatures":55}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":55,"outFeatures":55}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":21725}} {"id":"gen-grow-2749","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (117-feature input, 20 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2749\nComponents:\n - input (input) params={\"shape\":[1,117]}\n - fc1 (linear) params={\"inFeatures\":117,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":117,"outFeatures":900}},{"type":"update_params","name":"fc2","params":{"inFeatures":900,"outFeatures":900}},{"type":"update_params","name":"head","params":{"inFeatures":900,"outFeatures":20}}],"grade":{"pass":true,"score":80,"params":933300}} {"id":"gen-mlp-2750","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 484-feature input with 51 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2750\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":484,"outFeatures":831}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":831,"outFeatures":858}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":858,"outFeatures":51}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1158960}} {"id":"gen-ae-2751","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 247-dim input: encode down to a 51-dim bottleneck and decode back to 247. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2751\nComponents:\n - input (input) params={\"shape\":[1,247]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,247]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":247,"outFeatures":153}},{"componentType":"linear","name":"enc2","params":{"inFeatures":153,"outFeatures":51}},{"componentType":"linear","name":"dec1","params":{"inFeatures":51,"outFeatures":153}},{"componentType":"linear","name":"dec2","params":{"inFeatures":153,"outFeatures":247}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":91188}} {"id":"gen-cnn-2752","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x51x51 image with 96 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2752\nComponents:\n - input (input) params={\"shape\":[1,3,51,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,51,51]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":23,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":38,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":16,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":27276}} {"id":"gen-txf-2753","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 395-token sequences with 63 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2753\nComponents:\n - input (input) params={\"shape\":[1,395]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,395]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44371,"embeddingDim":224}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":224,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":10153920}} {"id":"gen-gqa-2754","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 382-token sequences with 18 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2754\nComponents:\n - input (input) params={\"shape\":[1,382]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,382]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25957,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":304,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7896400}} {"id":"gen-trim-2756","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3079-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2756\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - fc1 (linear) params={\"inFeatures\":212,\"outFeatures\":3079}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3079,\"outFeatures\":3079}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3079,\"outFeatures\":3079}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3079,\"outFeatures\":3079}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3079,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":212,"outFeatures":380}},{"type":"update_params","name":"fc2","params":{"inFeatures":380,"outFeatures":380}},{"type":"update_params","name":"fc3","params":{"inFeatures":380,"outFeatures":380}},{"type":"update_params","name":"fc4","params":{"inFeatures":380,"outFeatures":380}},{"type":"update_params","name":"head","params":{"inFeatures":380,"outFeatures":48}}],"grade":{"pass":true,"score":88,"params":532000}} {"id":"gen-norm-2757","family":"norm","seed":20260716,"spec":"This 176-feature, 18-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2757\nComponents:\n - input (input) params={\"shape\":[1,176]}\n - fc1 (linear) params={\"inFeatures\":176,\"outFeatures\":282}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":282,\"outFeatures\":181}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":181,\"outFeatures\":86}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":86,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":282}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":181}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":86}}],"grade":{"pass":true,"score":100,"params":117788}} {"id":"gen-tower-2758","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 148-feature user input and a 92-feature item input, each through its own 2-layer MLP tower ending at width 80, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2758\nComponents:\n - user_input (input) params={\"shape\":[1,148]}\n - item_input (input) params={\"shape\":[1,92]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,148]}},{"componentType":"input","name":"item_input","params":{"shape":[1,92]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":148,"outFeatures":80}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":80,"outFeatures":80}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":92,"outFeatures":80}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":80,"outFeatures":80}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":32160}} {"id":"gen-grow-2759","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (82-feature input, 31 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2759\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":82,"outFeatures":1318}},{"type":"update_params","name":"fc2","params":{"inFeatures":1318,"outFeatures":1318}},{"type":"update_params","name":"head","params":{"inFeatures":1318,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":1886058}} {"id":"gen-mlp-2760","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 74-feature input with 77 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2760\nComponents:\n - input (input) params={\"shape\":[1,74]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,74]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":74,"outFeatures":982}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":982,"outFeatures":91}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":91,"outFeatures":141}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":141,"outFeatures":301}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":301,"outFeatures":531}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":531,"outFeatures":808}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":808,"outFeatures":77}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":868397}} {"id":"gen-ae-2761","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2033-dim input: encode down to a 126-dim bottleneck and decode back to 2033. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2761\nComponents:\n - input (input) params={\"shape\":[1,2033]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2033]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2033,"outFeatures":885}},{"componentType":"linear","name":"enc2","params":{"inFeatures":885,"outFeatures":126}},{"componentType":"linear","name":"dec1","params":{"inFeatures":126,"outFeatures":885}},{"componentType":"linear","name":"dec2","params":{"inFeatures":885,"outFeatures":2033}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3821430}} {"id":"gen-cnn-2762","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x54x54 image with 11 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2762\nComponents:\n - input (input) params={\"shape\":[1,3,54,54]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,54,54]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":23,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":40,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":43741}} {"id":"gen-txf-2763","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 399-token sequences with 7 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2763\nComponents:\n - input (input) params={\"shape\":[1,399]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,399]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17511,"embeddingDim":64}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":64,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":1137536}} {"id":"gen-gqa-2764","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 205-token sequences with 72 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2764\nComponents:\n - input (input) params={\"shape\":[1,205]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,205]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32184,"embeddingDim":528}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":528,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[528]}},{"componentType":"linear","name":"head","params":{"inFeatures":528,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":17031168}} {"id":"gen-trim-2766","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2525-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2766\nComponents:\n - input (input) params={\"shape\":[1,249]}\n - fc1 (linear) params={\"inFeatures\":249,\"outFeatures\":2525}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2525,\"outFeatures\":2525}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2525,\"outFeatures\":2525}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2525,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":249,"outFeatures":311}},{"type":"update_params","name":"fc2","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"fc3","params":{"inFeatures":311,"outFeatures":311}},{"type":"update_params","name":"head","params":{"inFeatures":311,"outFeatures":49}}],"grade":{"pass":true,"score":84,"params":286120}} {"id":"gen-norm-2767","family":"norm","seed":20260716,"spec":"This 80-feature, 7-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2767\nComponents:\n - input (input) params={\"shape\":[1,80]}\n - fc1 (linear) params={\"inFeatures\":80,\"outFeatures\":439}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":439,\"outFeatures\":185}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":185,\"outFeatures\":292}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":292,\"outFeatures\":302}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":302,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":439}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":185}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":292}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":302}}],"grade":{"pass":true,"score":100,"params":260653}} {"id":"gen-tower-2768","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 24-feature user input and a 252-feature item input, each through its own 2-layer MLP tower ending at width 491, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2768\nComponents:\n - user_input (input) params={\"shape\":[1,24]}\n - item_input (input) params={\"shape\":[1,252]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,24]}},{"componentType":"input","name":"item_input","params":{"shape":[1,252]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":24,"outFeatures":491}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":491,"outFeatures":491}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":252,"outFeatures":491}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":491,"outFeatures":491}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":982,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":618660}} {"id":"gen-grow-2769","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (36-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2769\nComponents:\n - input (input) params={\"shape\":[1,36]}\n - fc1 (linear) params={\"inFeatures\":36,\"outFeatures\":24}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":24,\"outFeatures\":24}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":24,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":36,"outFeatures":1108}},{"type":"update_params","name":"fc2","params":{"inFeatures":1108,"outFeatures":1108}},{"type":"update_params","name":"head","params":{"inFeatures":1108,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1271984}} {"id":"gen-mlp-2770","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 347-feature input with 85 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2770\nComponents:\n - input (input) params={\"shape\":[1,347]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,347]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":347,"outFeatures":524}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":524,"outFeatures":217}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":217,"outFeatures":1001}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1001,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":597838}} {"id":"gen-ae-2771","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 251-dim input: encode down to a 209-dim bottleneck and decode back to 251. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2771\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,251]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":251,"outFeatures":572}},{"componentType":"linear","name":"enc2","params":{"inFeatures":572,"outFeatures":209}},{"componentType":"linear","name":"dec1","params":{"inFeatures":209,"outFeatures":572}},{"componentType":"linear","name":"dec2","params":{"inFeatures":572,"outFeatures":251}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":526240}} {"id":"gen-cnn-2772","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 54 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2772\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":8,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":8,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":1917}} {"id":"gen-txf-2773","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 162-token sequences with 94 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2773\nComponents:\n - input (input) params={\"shape\":[1,162]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,162]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27349,"embeddingDim":368}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":368,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":368,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":368,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":11724112}} {"id":"gen-gqa-2774","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 435-token sequences with 43 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2774\nComponents:\n - input (input) params={\"shape\":[1,435]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,435]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19579,"embeddingDim":320}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[320]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":320,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[320]}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":43}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":6279040}} {"id":"gen-fix-2775","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (142) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2775\nComponents:\n - input (input) params={\"shape\":[1,271]}\n - embed (embedding) params={\"numEmbeddings\":35933,\"embeddingDim\":142}\n - attn (multiHeadAttention) params={\"embedDim\":142,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":142,\"outFeatures\":54}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5190810}} {"id":"gen-trim-2776","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2967-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2776\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":2967}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2967,\"outFeatures\":2967}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2967,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":82,"outFeatures":165}},{"type":"update_params","name":"fc2","params":{"inFeatures":165,"outFeatures":165}},{"type":"update_params","name":"head","params":{"inFeatures":165,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":47025}} {"id":"gen-norm-2777","family":"norm","seed":20260716,"spec":"This 240-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2777\nComponents:\n - input (input) params={\"shape\":[1,240]}\n - fc1 (linear) params={\"inFeatures\":240,\"outFeatures\":368}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":368,\"outFeatures\":105}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":105,\"outFeatures\":251}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":251,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":368}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":105}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":251}}],"grade":{"pass":true,"score":100,"params":160845}} {"id":"gen-tower-2778","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 155-feature user input and a 217-feature item input, each through its own 2-layer MLP tower ending at width 271, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2778\nComponents:\n - user_input (input) params={\"shape\":[1,155]}\n - item_input (input) params={\"shape\":[1,217]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,155]}},{"componentType":"input","name":"item_input","params":{"shape":[1,217]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":155,"outFeatures":271}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":217,"outFeatures":271}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":271,"outFeatures":271}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":542,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":248236}} {"id":"gen-grow-2779","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (128-feature input, 26 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2779\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - fc1 (linear) params={\"inFeatures\":128,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":128,"outFeatures":1112}},{"type":"update_params","name":"fc2","params":{"inFeatures":1112,"outFeatures":1112}},{"type":"update_params","name":"head","params":{"inFeatures":1112,"outFeatures":26}}],"grade":{"pass":true,"score":80,"params":1407792}} {"id":"gen-mlp-2780","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 291-feature input with 72 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2780\nComponents:\n - input (input) params={\"shape\":[1,291]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,291]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":291,"outFeatures":212}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":212,"outFeatures":1014}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":1014,"outFeatures":921}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":921,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1276866}} {"id":"gen-ae-2781","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1462-dim input: encode down to a 25-dim bottleneck and decode back to 1462. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2781\nComponents:\n - input (input) params={\"shape\":[1,1462]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1462]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1462,"outFeatures":743}},{"componentType":"linear","name":"enc2","params":{"inFeatures":743,"outFeatures":25}},{"componentType":"linear","name":"dec1","params":{"inFeatures":25,"outFeatures":743}},{"componentType":"linear","name":"dec2","params":{"inFeatures":743,"outFeatures":1462}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2209682}} {"id":"gen-cnn-2782","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 28 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2782\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":50,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":50,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":31,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":55,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":55226}} {"id":"gen-txf-2783","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 402-token sequences with 20 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2783\nComponents:\n - input (input) params={\"shape\":[1,402]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,402]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48940,"embeddingDim":60}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":60,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":60,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":60,"outFeatures":20}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2966400}} {"id":"gen-gqa-2784","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 452-token sequences with 53 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2784\nComponents:\n - input (input) params={\"shape\":[1,452]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,452]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21003,"embeddingDim":704}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":704,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[704]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":704,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[704]}},{"componentType":"linear","name":"head","params":{"inFeatures":704,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":14823424}} {"id":"gen-trim-2786","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2267-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2786\nComponents:\n - input (input) params={\"shape\":[1,54]}\n - fc1 (linear) params={\"inFeatures\":54,\"outFeatures\":2267}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2267,\"outFeatures\":2267}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2267,\"outFeatures\":2267}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2267,\"outFeatures\":2267}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2267,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":54,"outFeatures":360}},{"type":"update_params","name":"fc2","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"fc3","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"fc4","params":{"inFeatures":360,"outFeatures":360}},{"type":"update_params","name":"head","params":{"inFeatures":360,"outFeatures":12}}],"grade":{"pass":true,"score":88,"params":412560}} {"id":"gen-norm-2787","family":"norm","seed":20260716,"spec":"This 230-feature, 57-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2787\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - fc1 (linear) params={\"inFeatures\":230,\"outFeatures\":389}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":389,\"outFeatures\":84}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":84,\"outFeatures\":247}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":247,\"outFeatures\":57}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":389}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":84}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":247}}],"grade":{"pass":true,"score":100,"params":156973}} {"id":"gen-tower-2788","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 53-feature user input and a 26-feature item input, each through its own 2-layer MLP tower ending at width 148, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2788\nComponents:\n - user_input (input) params={\"shape\":[1,53]}\n - item_input (input) params={\"shape\":[1,26]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,53]}},{"componentType":"input","name":"item_input","params":{"shape":[1,26]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":53,"outFeatures":148}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":148,"outFeatures":148}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":26,"outFeatures":148}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":148,"outFeatures":148}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":296,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":55796}} {"id":"gen-grow-2789","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (27-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2789\nComponents:\n - input (input) params={\"shape\":[1,27]}\n - fc1 (linear) params={\"inFeatures\":27,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":27,"outFeatures":1059}},{"type":"update_params","name":"fc2","params":{"inFeatures":1059,"outFeatures":1059}},{"type":"update_params","name":"head","params":{"inFeatures":1059,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1170195}} {"id":"gen-mlp-2790","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 39-feature input with 104 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2790\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,39]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":39,"outFeatures":960}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":960,"outFeatures":455}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":455,"outFeatures":104}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":521560}} {"id":"gen-ae-2791","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1120-dim input: encode down to a 181-dim bottleneck and decode back to 1120. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2791\nComponents:\n - input (input) params={\"shape\":[1,1120]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1120]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1120,"outFeatures":311}},{"componentType":"linear","name":"enc2","params":{"inFeatures":311,"outFeatures":181}},{"componentType":"linear","name":"dec1","params":{"inFeatures":181,"outFeatures":311}},{"componentType":"linear","name":"dec2","params":{"inFeatures":311,"outFeatures":1120}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":809222}} {"id":"gen-cnn-2792","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 99 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2792\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":36,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":9531}} {"id":"gen-txf-2793","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 442-token sequences with 70 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2793\nComponents:\n - input (input) params={\"shape\":[1,442]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,442]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16553,"embeddingDim":220}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":220,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":220,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":220,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4044260}} {"id":"gen-gqa-2794","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 211-token sequences with 91 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2794\nComponents:\n - input (input) params={\"shape\":[1,211]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,211]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13060,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3997904}} {"id":"gen-trim-2796","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2385-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2796\nComponents:\n - input (input) params={\"shape\":[1,35]}\n - fc1 (linear) params={\"inFeatures\":35,\"outFeatures\":2385}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2385,\"outFeatures\":2385}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2385,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":35,"outFeatures":328}},{"type":"update_params","name":"fc2","params":{"inFeatures":328,"outFeatures":328}},{"type":"update_params","name":"head","params":{"inFeatures":328,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":133824}} {"id":"gen-norm-2797","family":"norm","seed":20260716,"spec":"This 153-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2797\nComponents:\n - input (input) params={\"shape\":[1,153]}\n - fc1 (linear) params={\"inFeatures\":153,\"outFeatures\":406}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":406,\"outFeatures\":320}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":320,\"outFeatures\":231}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":231,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":406}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":320}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":231}}],"grade":{"pass":true,"score":100,"params":281435}} {"id":"gen-tower-2798","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 159-feature user input and a 118-feature item input, each through its own 2-layer MLP tower ending at width 511, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2798\nComponents:\n - user_input (input) params={\"shape\":[1,159]}\n - item_input (input) params={\"shape\":[1,118]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,159]}},{"componentType":"input","name":"item_input","params":{"shape":[1,118]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":159,"outFeatures":511}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":511,"outFeatures":511}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":118,"outFeatures":511}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":511,"outFeatures":511}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":1022,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":664811}} {"id":"gen-grow-2799","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (53-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2799\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":53,"outFeatures":915}},{"type":"update_params","name":"fc2","params":{"inFeatures":915,"outFeatures":915}},{"type":"update_params","name":"head","params":{"inFeatures":915,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":895785}} {"id":"gen-mlp-2800","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 224-feature input with 140 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2800\nComponents:\n - input (input) params={\"shape\":[1,224]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,224]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":224,"outFeatures":91}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":91,"outFeatures":996}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":996,"outFeatures":140}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":250460}} {"id":"gen-ae-2801","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 484-dim input: encode down to a 135-dim bottleneck and decode back to 484. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2801\nComponents:\n - input (input) params={\"shape\":[1,484]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,484]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":484,"outFeatures":413}},{"componentType":"linear","name":"enc2","params":{"inFeatures":413,"outFeatures":135}},{"componentType":"linear","name":"dec1","params":{"inFeatures":135,"outFeatures":413}},{"componentType":"linear","name":"dec2","params":{"inFeatures":413,"outFeatures":484}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":511294}} {"id":"gen-cnn-2802","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x25x25 image with 75 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2802\nComponents:\n - input (input) params={\"shape\":[1,3,25,25]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,25,25]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":55,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":45,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":35,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":63609}} {"id":"gen-txf-2803","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 473-token sequences with 28 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2803\nComponents:\n - input (input) params={\"shape\":[1,473]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,473]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8161,"embeddingDim":90}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":90,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":90,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":90,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":801810}} {"id":"gen-gqa-2804","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 385-token sequences with 90 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2804\nComponents:\n - input (input) params={\"shape\":[1,385]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,385]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18896,"embeddingDim":224}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":224,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[224]}},{"componentType":"linear","name":"head","params":{"inFeatures":224,"outFeatures":90}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4252864}} {"id":"gen-trim-2806","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1842-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2806\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - fc1 (linear) params={\"inFeatures\":125,\"outFeatures\":1842}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1842,\"outFeatures\":1842}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1842,\"outFeatures\":1842}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1842,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":125,"outFeatures":92}},{"type":"update_params","name":"fc2","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"fc3","params":{"inFeatures":92,"outFeatures":92}},{"type":"update_params","name":"head","params":{"inFeatures":92,"outFeatures":12}}],"grade":{"pass":true,"score":84,"params":29532}} {"id":"gen-norm-2807","family":"norm","seed":20260716,"spec":"This 155-feature, 48-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2807\nComponents:\n - input (input) params={\"shape\":[1,155]}\n - fc1 (linear) params={\"inFeatures\":155,\"outFeatures\":304}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":304,\"outFeatures\":299}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":299,\"outFeatures\":320}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":320,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":304}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":299}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":320}}],"grade":{"pass":true,"score":100,"params":249056}} {"id":"gen-tower-2808","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 200-feature user input and a 50-feature item input, each through its own 2-layer MLP tower ending at width 131, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2808\nComponents:\n - user_input (input) params={\"shape\":[1,200]}\n - item_input (input) params={\"shape\":[1,50]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,200]}},{"componentType":"input","name":"item_input","params":{"shape":[1,50]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":200,"outFeatures":131}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":131,"outFeatures":131}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":50,"outFeatures":131}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":131,"outFeatures":131}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":262,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":67334}} {"id":"gen-grow-2809","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (104-feature input, 2 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2809\nComponents:\n - input (input) params={\"shape\":[1,104]}\n - fc1 (linear) params={\"inFeatures\":104,\"outFeatures\":5}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":5,\"outFeatures\":5}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":5,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":104,"outFeatures":723}},{"type":"update_params","name":"fc2","params":{"inFeatures":723,"outFeatures":723}},{"type":"update_params","name":"head","params":{"inFeatures":723,"outFeatures":2}}],"grade":{"pass":true,"score":80,"params":599367}} {"id":"gen-mlp-2810","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 455-feature input with 144 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2810\nComponents:\n - input (input) params={\"shape\":[1,455]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,455]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":455,"outFeatures":196}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":196,"outFeatures":699}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":699,"outFeatures":204}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":204,"outFeatures":136}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":144}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":416108}} {"id":"gen-ae-2811","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 107-dim input: encode down to a 143-dim bottleneck and decode back to 107. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2811\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,107]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":107,"outFeatures":1017}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1017,"outFeatures":143}},{"componentType":"linear","name":"dec1","params":{"inFeatures":143,"outFeatures":1017}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1017,"outFeatures":107}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":508500}} {"id":"gen-cnn-2812","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 88 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2812\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":32,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":32,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":39,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":35,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":88}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":45337}} {"id":"gen-txf-2813","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 87-token sequences with 17 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2813\nComponents:\n - input (input) params={\"shape\":[1,87]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,87]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37152,"embeddingDim":232}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":232,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":232,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":9053800}} {"id":"gen-gqa-2814","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 339-token sequences with 99 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2814\nComponents:\n - input (input) params={\"shape\":[1,339]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,339]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48905,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":14897216}} {"id":"gen-fix-2815","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (452) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2815\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - embed (embedding) params={\"numEmbeddings\":23125,\"embeddingDim\":452}\n - attn (multiHeadAttention) params={\"embedDim\":452,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":452,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":11306328}} {"id":"gen-trim-2816","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2639-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2816\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - fc1 (linear) params={\"inFeatures\":236,\"outFeatures\":2639}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2639,\"outFeatures\":2639}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2639,\"outFeatures\":2639}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2639,\"outFeatures\":2639}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2639,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":236,"outFeatures":160}},{"type":"update_params","name":"fc2","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"fc3","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"fc4","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"head","params":{"inFeatures":160,"outFeatures":37}}],"grade":{"pass":true,"score":88,"params":120480}} {"id":"gen-norm-2817","family":"norm","seed":20260716,"spec":"This 238-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2817\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - fc1 (linear) params={\"inFeatures\":238,\"outFeatures\":191}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":191,\"outFeatures\":241}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":241,\"outFeatures\":200}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":200,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":191}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":241}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":200}}],"grade":{"pass":true,"score":100,"params":154489}} {"id":"gen-tower-2818","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 66-feature user input and a 180-feature item input, each through its own 2-layer MLP tower ending at width 75, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2818\nComponents:\n - user_input (input) params={\"shape\":[1,66]}\n - item_input (input) params={\"shape\":[1,180]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,66]}},{"componentType":"input","name":"item_input","params":{"shape":[1,180]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":66,"outFeatures":75}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":75,"outFeatures":75}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":180,"outFeatures":75}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":75,"outFeatures":75}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":150,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29850}} {"id":"gen-grow-2819","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (50-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2819\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":1083}},{"type":"update_params","name":"fc2","params":{"inFeatures":1083,"outFeatures":1083}},{"type":"update_params","name":"head","params":{"inFeatures":1083,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1249782}} {"id":"gen-mlp-2820","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 511-feature input with 185 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2820\nComponents:\n - input (input) params={\"shape\":[1,511]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,511]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":511,"outFeatures":337}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":337,"outFeatures":888}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":888,"outFeatures":185}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":635743}} {"id":"gen-ae-2821","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2033-dim input: encode down to a 164-dim bottleneck and decode back to 2033. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2821\nComponents:\n - input (input) params={\"shape\":[1,2033]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2033]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2033,"outFeatures":175}},{"componentType":"linear","name":"enc2","params":{"inFeatures":175,"outFeatures":164}},{"componentType":"linear","name":"dec1","params":{"inFeatures":164,"outFeatures":175}},{"componentType":"linear","name":"dec2","params":{"inFeatures":175,"outFeatures":2033}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":768950}} {"id":"gen-cnn-2822","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x55x55 image with 8 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2822\nComponents:\n - input (input) params={\"shape\":[1,3,55,55]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,55,55]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":48,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":18,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":14002}} {"id":"gen-txf-2823","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 330-token sequences with 2 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2823\nComponents:\n - input (input) params={\"shape\":[1,330]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,330]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40609,"embeddingDim":106}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":106,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":106,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":106,"outFeatures":2}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":4394654}} {"id":"gen-gqa-2824","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 123-token sequences with 15 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2824\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,123]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12086,"embeddingDim":688}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":688,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[688]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":688,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[688]}},{"componentType":"linear","name":"head","params":{"inFeatures":688,"outFeatures":15}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":8325488}} {"id":"gen-trim-2826","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3956-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2826\nComponents:\n - input (input) params={\"shape\":[1,246]}\n - fc1 (linear) params={\"inFeatures\":246,\"outFeatures\":3956}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3956,\"outFeatures\":3956}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3956,\"outFeatures\":3956}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3956,\"outFeatures\":3956}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3956,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":246,"outFeatures":254}},{"type":"update_params","name":"fc2","params":{"inFeatures":254,"outFeatures":254}},{"type":"update_params","name":"fc3","params":{"inFeatures":254,"outFeatures":254}},{"type":"update_params","name":"fc4","params":{"inFeatures":254,"outFeatures":254}},{"type":"update_params","name":"head","params":{"inFeatures":254,"outFeatures":32}}],"grade":{"pass":true,"score":88,"params":264160}} {"id":"gen-norm-2827","family":"norm","seed":20260716,"spec":"This 33-feature, 44-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2827\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":326}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":326,\"outFeatures\":223}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":223,\"outFeatures\":453}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":453,\"outFeatures\":234}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":234,\"outFeatures\":103}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":103,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":326}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":223}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":453}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":234}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":103}}],"grade":{"pass":true,"score":100,"params":319111}} {"id":"gen-tower-2828","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 124-feature user input and a 109-feature item input, each through its own 2-layer MLP tower ending at width 409, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2828\nComponents:\n - user_input (input) params={\"shape\":[1,124]}\n - item_input (input) params={\"shape\":[1,109]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,124]}},{"componentType":"input","name":"item_input","params":{"shape":[1,109]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":124,"outFeatures":409}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":409,"outFeatures":409}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":109,"outFeatures":409}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":409,"outFeatures":409}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":818,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":430677}} {"id":"gen-grow-2829","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (50-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2829\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":1171}},{"type":"update_params","name":"fc2","params":{"inFeatures":1171,"outFeatures":1171}},{"type":"update_params","name":"head","params":{"inFeatures":1171,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":1475460}} {"id":"gen-mlp-2830","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 355-feature input with 54 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2830\nComponents:\n - input (input) params={\"shape\":[1,355]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,355]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":355,"outFeatures":213}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":213,"outFeatures":178}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":178,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":123141}} {"id":"gen-ae-2831","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1477-dim input: encode down to a 175-dim bottleneck and decode back to 1477. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2831\nComponents:\n - input (input) params={\"shape\":[1,1477]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1477]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1477,"outFeatures":156}},{"componentType":"linear","name":"enc2","params":{"inFeatures":156,"outFeatures":175}},{"componentType":"linear","name":"dec1","params":{"inFeatures":175,"outFeatures":156}},{"componentType":"linear","name":"dec2","params":{"inFeatures":156,"outFeatures":1477}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":515424}} {"id":"gen-cnn-2832","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 9 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2832\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":53,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":41,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":55,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":41778}} {"id":"gen-txf-2833","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 125-token sequences with 7 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2833\nComponents:\n - input (input) params={\"shape\":[1,125]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,125]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27573,"embeddingDim":240}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":240,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":240,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":240,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7080000}} {"id":"gen-gqa-2834","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 472-token sequences with 25 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2834\nComponents:\n - input (input) params={\"shape\":[1,472]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,472]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8069,"embeddingDim":304}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":304,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[304]}},{"componentType":"linear","name":"head","params":{"inFeatures":304,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":2460576}} {"id":"gen-fix-2835","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (342) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2835\nComponents:\n - input (input) params={\"shape\":[1,247]}\n - embed (embedding) params={\"numEmbeddings\":46219,\"embeddingDim\":342}\n - attn (multiHeadAttention) params={\"embedDim\":342,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":342,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":16276122}} {"id":"gen-trim-2836","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3072-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2836\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - fc1 (linear) params={\"inFeatures\":230,\"outFeatures\":3072}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3072,\"outFeatures\":3072}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3072,\"outFeatures\":3072}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3072,\"outFeatures\":22}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":230,"outFeatures":126}},{"type":"update_params","name":"fc2","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"fc3","params":{"inFeatures":126,"outFeatures":126}},{"type":"update_params","name":"head","params":{"inFeatures":126,"outFeatures":22}}],"grade":{"pass":true,"score":84,"params":63504}} {"id":"gen-norm-2837","family":"norm","seed":20260716,"spec":"This 207-feature, 74-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2837\nComponents:\n - input (input) params={\"shape\":[1,207]}\n - fc1 (linear) params={\"inFeatures\":207,\"outFeatures\":467}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":467,\"outFeatures\":90}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":90,\"outFeatures\":39}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":39,\"outFeatures\":402}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":402,\"outFeatures\":92}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":92,\"outFeatures\":74}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":467}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":90}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":39}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":402}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":92}}],"grade":{"pass":true,"score":100,"params":201679}} {"id":"gen-tower-2838","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 228-feature user input and a 150-feature item input, each through its own 2-layer MLP tower ending at width 339, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2838\nComponents:\n - user_input (input) params={\"shape\":[1,228]}\n - item_input (input) params={\"shape\":[1,150]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,228]}},{"componentType":"input","name":"item_input","params":{"shape":[1,150]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":228,"outFeatures":339}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":150,"outFeatures":339}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":339,"outFeatures":339}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":678,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":358662}} {"id":"gen-grow-2839","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (106-feature input, 50 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2839\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":106,"outFeatures":752}},{"type":"update_params","name":"fc2","params":{"inFeatures":752,"outFeatures":752}},{"type":"update_params","name":"head","params":{"inFeatures":752,"outFeatures":50}}],"grade":{"pass":true,"score":80,"params":682816}} {"id":"gen-mlp-2840","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 451-feature input with 145 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2840\nComponents:\n - input (input) params={\"shape\":[1,451]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,451]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":451,"outFeatures":674}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":674,"outFeatures":615}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":615,"outFeatures":516}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":516,"outFeatures":901}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":901,"outFeatures":145}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1631385}} {"id":"gen-ae-2841","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 672-dim input: encode down to a 62-dim bottleneck and decode back to 672. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2841\nComponents:\n - input (input) params={\"shape\":[1,672]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,672]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":672,"outFeatures":875}},{"componentType":"linear","name":"enc2","params":{"inFeatures":875,"outFeatures":62}},{"componentType":"linear","name":"dec1","params":{"inFeatures":62,"outFeatures":875}},{"componentType":"linear","name":"dec2","params":{"inFeatures":875,"outFeatures":672}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1284500}} {"id":"gen-cnn-2842","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x59x59 image with 70 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2842\nComponents:\n - input (input) params={\"shape\":[1,3,59,59]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,59,59]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":61,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":57,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":56763}} {"id":"gen-txf-2843","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 107-token sequences with 13 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2843\nComponents:\n - input (input) params={\"shape\":[1,107]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,107]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41504,"embeddingDim":216}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":216,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":216,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":216,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":9340920}} {"id":"gen-gqa-2844","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 286-token sequences with 22 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2844\nComponents:\n - input (input) params={\"shape\":[1,286]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,286]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47998,"embeddingDim":128}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":128,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[128]}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":6146560}} {"id":"gen-fix-2845","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (90) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2845\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - embed (embedding) params={\"numEmbeddings\":38994,\"embeddingDim\":90}\n - attn (multiHeadAttention) params={\"embedDim\":90,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":90,\"outFeatures\":99}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":3550770}} {"id":"gen-trim-2846","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3814-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2846\nComponents:\n - input (input) params={\"shape\":[1,221]}\n - fc1 (linear) params={\"inFeatures\":221,\"outFeatures\":3814}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3814,\"outFeatures\":3814}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3814,\"outFeatures\":3814}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3814,\"outFeatures\":3814}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3814,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":221,"outFeatures":207}},{"type":"update_params","name":"fc2","params":{"inFeatures":207,"outFeatures":207}},{"type":"update_params","name":"fc3","params":{"inFeatures":207,"outFeatures":207}},{"type":"update_params","name":"fc4","params":{"inFeatures":207,"outFeatures":207}},{"type":"update_params","name":"head","params":{"inFeatures":207,"outFeatures":44}}],"grade":{"pass":true,"score":88,"params":183402}} {"id":"gen-norm-2847","family":"norm","seed":20260716,"spec":"This 16-feature, 42-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2847\nComponents:\n - input (input) params={\"shape\":[1,16]}\n - fc1 (linear) params={\"inFeatures\":16,\"outFeatures\":118}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":118,\"outFeatures\":265}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":265,\"outFeatures\":53}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":53,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":118}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":265}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":53}}],"grade":{"pass":true,"score":100,"params":49429}} {"id":"gen-tower-2848","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 182-feature user input and a 131-feature item input, each through its own 2-layer MLP tower ending at width 37, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2848\nComponents:\n - user_input (input) params={\"shape\":[1,182]}\n - item_input (input) params={\"shape\":[1,131]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,182]}},{"componentType":"input","name":"item_input","params":{"shape":[1,131]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":182,"outFeatures":37}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":37,"outFeatures":37}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":131,"outFeatures":37}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":37,"outFeatures":37}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":74,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":14393}} {"id":"gen-grow-2849","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (37-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2849\nComponents:\n - input (input) params={\"shape\":[1,37]}\n - fc1 (linear) params={\"inFeatures\":37,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":37,"outFeatures":1228}},{"type":"update_params","name":"fc2","params":{"inFeatures":1228,"outFeatures":1228}},{"type":"update_params","name":"head","params":{"inFeatures":1228,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":1575524}} {"id":"gen-mlp-2850","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 178-feature input with 182 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2850\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,178]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":178,"outFeatures":510}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":510,"outFeatures":839}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":839,"outFeatures":23}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":23,"outFeatures":654}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":654,"outFeatures":1003}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1003,"outFeatures":182}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1391517}} {"id":"gen-ae-2851","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1070-dim input: encode down to a 201-dim bottleneck and decode back to 1070. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2851\nComponents:\n - input (input) params={\"shape\":[1,1070]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1070]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1070,"outFeatures":341}},{"componentType":"linear","name":"enc2","params":{"inFeatures":341,"outFeatures":201}},{"componentType":"linear","name":"dec1","params":{"inFeatures":201,"outFeatures":341}},{"componentType":"linear","name":"dec2","params":{"inFeatures":341,"outFeatures":1070}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":866822}} {"id":"gen-cnn-2852","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 42 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2852\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":15,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":56,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":28320}} {"id":"gen-txf-2853","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 365-token sequences with 17 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2853\nComponents:\n - input (input) params={\"shape\":[1,365]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,365]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":12685,"embeddingDim":110}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":110,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":110,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":17}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1494020}} {"id":"gen-gqa-2854","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 375-token sequences with 26 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2854\nComponents:\n - input (input) params={\"shape\":[1,375]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,375]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18538,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3267264}} {"id":"gen-trim-2856","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2578-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2856\nComponents:\n - input (input) params={\"shape\":[1,208]}\n - fc1 (linear) params={\"inFeatures\":208,\"outFeatures\":2578}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2578,\"outFeatures\":2578}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2578,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":208,"outFeatures":246}},{"type":"update_params","name":"fc2","params":{"inFeatures":246,"outFeatures":246}},{"type":"update_params","name":"head","params":{"inFeatures":246,"outFeatures":13}}],"grade":{"pass":true,"score":80,"params":114882}} {"id":"gen-norm-2857","family":"norm","seed":20260716,"spec":"This 68-feature, 2-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2857\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":293}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":293,\"outFeatures\":187}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":187,\"outFeatures\":80}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":80,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":293}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":187}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":80}}],"grade":{"pass":true,"score":100,"params":89835}} {"id":"gen-tower-2858","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 219-feature user input and a 32-feature item input, each through its own 2-layer MLP tower ending at width 354, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2858\nComponents:\n - user_input (input) params={\"shape\":[1,219]}\n - item_input (input) params={\"shape\":[1,32]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,219]}},{"componentType":"input","name":"item_input","params":{"shape":[1,32]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":219,"outFeatures":354}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":354,"outFeatures":354}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":32,"outFeatures":354}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":354,"outFeatures":354}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":708,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":340194}} {"id":"gen-grow-2859","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (65-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2859\nComponents:\n - input (input) params={\"shape\":[1,65]}\n - fc1 (linear) params={\"inFeatures\":65,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":65,"outFeatures":1086}},{"type":"update_params","name":"fc2","params":{"inFeatures":1086,"outFeatures":1086}},{"type":"update_params","name":"head","params":{"inFeatures":1086,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":1285824}} {"id":"gen-mlp-2860","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 225-feature input with 22 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2860\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,225]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":225,"outFeatures":708}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":708,"outFeatures":447}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":447,"outFeatures":735}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":735,"outFeatures":961}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":961,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1531798}} {"id":"gen-ae-2861","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1508-dim input: encode down to a 24-dim bottleneck and decode back to 1508. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2861\nComponents:\n - input (input) params={\"shape\":[1,1508]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1508]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1508,"outFeatures":949}},{"componentType":"linear","name":"enc2","params":{"inFeatures":949,"outFeatures":24}},{"componentType":"linear","name":"dec1","params":{"inFeatures":24,"outFeatures":949}},{"componentType":"linear","name":"dec2","params":{"inFeatures":949,"outFeatures":1508}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2907736}} {"id":"gen-cnn-2862","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 64 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2862\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":47,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":19,"outFeatures":64}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":56656}} {"id":"gen-txf-2863","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 110-token sequences with 99 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2863\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,110]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42353,"embeddingDim":152}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":152,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":152,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":99}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":6637536}} {"id":"gen-gqa-2864","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 457-token sequences with 53 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2864\nComponents:\n - input (input) params={\"shape\":[1,457]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,457]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45466,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":53}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":3641520}} {"id":"gen-fix-2865","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (434) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2865\nComponents:\n - input (input) params={\"shape\":[1,147]}\n - embed (embedding) params={\"numEmbeddings\":11760,\"embeddingDim\":434}\n - attn (multiHeadAttention) params={\"embedDim\":434,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":434,\"outFeatures\":9}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5861170}} {"id":"gen-trim-2866","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3574-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2866\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - fc1 (linear) params={\"inFeatures\":129,\"outFeatures\":3574}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3574,\"outFeatures\":3574}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3574,\"outFeatures\":3574}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3574,\"outFeatures\":34}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":129,"outFeatures":149}},{"type":"update_params","name":"fc2","params":{"inFeatures":149,"outFeatures":149}},{"type":"update_params","name":"fc3","params":{"inFeatures":149,"outFeatures":149}},{"type":"update_params","name":"head","params":{"inFeatures":149,"outFeatures":34}}],"grade":{"pass":true,"score":84,"params":68689}} {"id":"gen-norm-2867","family":"norm","seed":20260716,"spec":"This 236-feature, 53-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2867\nComponents:\n - input (input) params={\"shape\":[1,236]}\n - fc1 (linear) params={\"inFeatures\":236,\"outFeatures\":335}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":335,\"outFeatures\":339}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":339,\"outFeatures\":309}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":309,\"outFeatures\":53}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":335}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":339}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":309}}],"grade":{"pass":true,"score":100,"params":313753}} {"id":"gen-tower-2868","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 249-feature user input and a 95-feature item input, each through its own 2-layer MLP tower ending at width 286, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2868\nComponents:\n - user_input (input) params={\"shape\":[1,249]}\n - item_input (input) params={\"shape\":[1,95]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,249]}},{"componentType":"input","name":"item_input","params":{"shape":[1,95]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":249,"outFeatures":286}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":286,"outFeatures":286}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":95,"outFeatures":286}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":286,"outFeatures":286}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":572,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":262548}} {"id":"gen-grow-2869","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (53-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2869\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - fc1 (linear) params={\"inFeatures\":53,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":53,"outFeatures":972}},{"type":"update_params","name":"fc2","params":{"inFeatures":972,"outFeatures":972}},{"type":"update_params","name":"head","params":{"inFeatures":972,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":1006992}} {"id":"gen-mlp-2870","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 129-feature input with 7 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2870\nComponents:\n - input (input) params={\"shape\":[1,129]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,129]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":129,"outFeatures":53}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":53,"outFeatures":569}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":569,"outFeatures":486}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":486,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":316930}} {"id":"gen-ae-2871","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1718-dim input: encode down to a 222-dim bottleneck and decode back to 1718. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2871\nComponents:\n - input (input) params={\"shape\":[1,1718]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1718]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1718,"outFeatures":385}},{"componentType":"linear","name":"enc2","params":{"inFeatures":385,"outFeatures":222}},{"componentType":"linear","name":"dec1","params":{"inFeatures":222,"outFeatures":385}},{"componentType":"linear","name":"dec2","params":{"inFeatures":385,"outFeatures":1718}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1493800}} {"id":"gen-cnn-2872","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 22 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2872\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":10,"outFeatures":22}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4783}} {"id":"gen-txf-2873","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 367-token sequences with 89 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2873\nComponents:\n - input (input) params={\"shape\":[1,367]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,367]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17570,"embeddingDim":192}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":192,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3537984}} {"id":"gen-gqa-2874","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 83-token sequences with 38 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2874\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,83]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49016,"embeddingDim":376}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":376,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[376]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":376,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[376]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":376,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[376]}},{"componentType":"linear","name":"head","params":{"inFeatures":376,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":18444304}} {"id":"gen-fix-2875","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (194) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2875\nComponents:\n - input (input) params={\"shape\":[1,337]}\n - embed (embedding) params={\"numEmbeddings\":21628,\"embeddingDim\":194}\n - attn (multiHeadAttention) params={\"embedDim\":194,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":194,\"outFeatures\":93}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":4364418}} {"id":"gen-trim-2876","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3392-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2876\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - fc1 (linear) params={\"inFeatures\":212,\"outFeatures\":3392}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3392,\"outFeatures\":3392}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3392,\"outFeatures\":3392}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3392,\"outFeatures\":3392}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3392,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":212,"outFeatures":160}},{"type":"update_params","name":"fc2","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"fc3","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"fc4","params":{"inFeatures":160,"outFeatures":160}},{"type":"update_params","name":"head","params":{"inFeatures":160,"outFeatures":39}}],"grade":{"pass":true,"score":88,"params":116960}} {"id":"gen-norm-2877","family":"norm","seed":20260716,"spec":"This 214-feature, 100-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2877\nComponents:\n - input (input) params={\"shape\":[1,214]}\n - fc1 (linear) params={\"inFeatures\":214,\"outFeatures\":306}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":306,\"outFeatures\":194}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":194,\"outFeatures\":83}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":83,\"outFeatures\":486}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":486,\"outFeatures\":100}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":306}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":194}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":83}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":486}}],"grade":{"pass":true,"score":100,"params":229888}} {"id":"gen-tower-2878","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 226-feature user input and a 100-feature item input, each through its own 2-layer MLP tower ending at width 391, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2878\nComponents:\n - user_input (input) params={\"shape\":[1,226]}\n - item_input (input) params={\"shape\":[1,100]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,226]}},{"componentType":"input","name":"item_input","params":{"shape":[1,100]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":226,"outFeatures":391}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":391,"outFeatures":391}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":100,"outFeatures":391}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":391,"outFeatures":391}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":782,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":434010}} {"id":"gen-grow-2879","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (68-feature input, 38 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2879\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":68,"outFeatures":967}},{"type":"update_params","name":"fc2","params":{"inFeatures":967,"outFeatures":967}},{"type":"update_params","name":"head","params":{"inFeatures":967,"outFeatures":38}}],"grade":{"pass":true,"score":80,"params":1037591}} {"id":"gen-mlp-2880","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 51-feature input with 81 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2880\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,51]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":51,"outFeatures":412}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":412,"outFeatures":979}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":979,"outFeatures":971}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":971,"outFeatures":427}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":427,"outFeatures":97}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":97,"outFeatures":31}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1836523}} {"id":"gen-ae-2881","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 177-dim input: encode down to a 216-dim bottleneck and decode back to 177. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2881\nComponents:\n - input (input) params={\"shape\":[1,177]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,177]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":177,"outFeatures":541}},{"componentType":"linear","name":"enc2","params":{"inFeatures":541,"outFeatures":216}},{"componentType":"linear","name":"dec1","params":{"inFeatures":216,"outFeatures":541}},{"componentType":"linear","name":"dec2","params":{"inFeatures":541,"outFeatures":177}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":425226}} {"id":"gen-cnn-2882","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x41x41 image with 47 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2882\nComponents:\n - input (input) params={\"shape\":[1,3,41,41]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,41,41]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":28,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":47}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":27584}} {"id":"gen-txf-2883","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 100-token sequences with 33 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2883\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,100]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33362,"embeddingDim":456}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":456,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":456,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":456,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":17723352}} {"id":"gen-gqa-2884","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 495-token sequences with 63 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2884\nComponents:\n - input (input) params={\"shape\":[1,495]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,495]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":32812,"embeddingDim":140}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":140,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[140]}},{"componentType":"linear","name":"head","params":{"inFeatures":140,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":4602500}} {"id":"gen-fix-2885","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (398) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2885\nComponents:\n - input (input) params={\"shape\":[1,373]}\n - embed (embedding) params={\"numEmbeddings\":25009,\"embeddingDim\":398}\n - attn (multiHeadAttention) params={\"embedDim\":398,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":398,\"outFeatures\":94}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":10624610}} {"id":"gen-trim-2886","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3486-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2886\nComponents:\n - input (input) params={\"shape\":[1,234]}\n - fc1 (linear) params={\"inFeatures\":234,\"outFeatures\":3486}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3486,\"outFeatures\":3486}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3486,\"outFeatures\":31}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":234,"outFeatures":108}},{"type":"update_params","name":"fc2","params":{"inFeatures":108,"outFeatures":108}},{"type":"update_params","name":"head","params":{"inFeatures":108,"outFeatures":31}}],"grade":{"pass":true,"score":80,"params":40284}} {"id":"gen-norm-2887","family":"norm","seed":20260716,"spec":"This 226-feature, 16-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2887\nComponents:\n - input (input) params={\"shape\":[1,226]}\n - fc1 (linear) params={\"inFeatures\":226,\"outFeatures\":416}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":416,\"outFeatures\":223}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":223,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":416}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":223}}],"grade":{"pass":true,"score":94,"params":190352}} {"id":"gen-tower-2888","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 245-feature user input and a 83-feature item input, each through its own 2-layer MLP tower ending at width 375, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2888\nComponents:\n - user_input (input) params={\"shape\":[1,245]}\n - item_input (input) params={\"shape\":[1,83]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,245]}},{"componentType":"input","name":"item_input","params":{"shape":[1,83]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":245,"outFeatures":375}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":83,"outFeatures":375}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":750,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":405000}} {"id":"gen-grow-2889","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (109-feature input, 6 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2889\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - fc1 (linear) params={\"inFeatures\":109,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":6}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":109,"outFeatures":686}},{"type":"update_params","name":"fc2","params":{"inFeatures":686,"outFeatures":686}},{"type":"update_params","name":"head","params":{"inFeatures":686,"outFeatures":6}}],"grade":{"pass":true,"score":80,"params":549486}} {"id":"gen-mlp-2890","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 153-feature input with 41 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2890\nComponents:\n - input (input) params={\"shape\":[1,153]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,153]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":153,"outFeatures":504}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":504,"outFeatures":901}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":901,"outFeatures":459}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":459,"outFeatures":622}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":622,"outFeatures":584}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":584,"outFeatures":111}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":111,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1662896}} {"id":"gen-ae-2891","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 648-dim input: encode down to a 240-dim bottleneck and decode back to 648. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2891\nComponents:\n - input (input) params={\"shape\":[1,648]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,648]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":648,"outFeatures":172}},{"componentType":"linear","name":"enc2","params":{"inFeatures":172,"outFeatures":240}},{"componentType":"linear","name":"dec1","params":{"inFeatures":240,"outFeatures":172}},{"componentType":"linear","name":"dec2","params":{"inFeatures":172,"outFeatures":648}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":305472}} {"id":"gen-cnn-2892","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 80 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2892\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":17,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":80}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":11643}} {"id":"gen-txf-2893","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 504-token sequences with 59 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2893\nComponents:\n - input (input) params={\"shape\":[1,504]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,504]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42544,"embeddingDim":84}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":84,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":3606876}} {"id":"gen-gqa-2894","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 183-token sequences with 72 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2894\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,183]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31625,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":560,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":17750320}} {"id":"gen-trim-2896","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2141-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2896\nComponents:\n - input (input) params={\"shape\":[1,146]}\n - fc1 (linear) params={\"inFeatures\":146,\"outFeatures\":2141}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2141,\"outFeatures\":2141}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2141,\"outFeatures\":2141}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2141,\"outFeatures\":2141}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2141,\"outFeatures\":2}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":146,"outFeatures":372}},{"type":"update_params","name":"fc2","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"fc3","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"fc4","params":{"inFeatures":372,"outFeatures":372}},{"type":"update_params","name":"head","params":{"inFeatures":372,"outFeatures":2}}],"grade":{"pass":true,"score":88,"params":470208}} {"id":"gen-norm-2897","family":"norm","seed":20260716,"spec":"This 29-feature, 27-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2897\nComponents:\n - input (input) params={\"shape\":[1,29]}\n - fc1 (linear) params={\"inFeatures\":29,\"outFeatures\":200}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":200,\"outFeatures\":478}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":478,\"outFeatures\":27}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":200}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":478}}],"grade":{"pass":true,"score":94,"params":114306}} {"id":"gen-tower-2898","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 130-feature user input and a 136-feature item input, each through its own 2-layer MLP tower ending at width 255, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2898\nComponents:\n - user_input (input) params={\"shape\":[1,130]}\n - item_input (input) params={\"shape\":[1,136]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,130]}},{"componentType":"input","name":"item_input","params":{"shape":[1,136]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":130,"outFeatures":255}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":255,"outFeatures":255}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":136,"outFeatures":255}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":255,"outFeatures":255}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":510,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":198390}} {"id":"gen-grow-2899","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (101-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2899\nComponents:\n - input (input) params={\"shape\":[1,101]}\n - fc1 (linear) params={\"inFeatures\":101,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":101,"outFeatures":1302}},{"type":"update_params","name":"fc2","params":{"inFeatures":1302,"outFeatures":1302}},{"type":"update_params","name":"head","params":{"inFeatures":1302,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":1844934}} {"id":"gen-mlp-2900","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 428-feature input with 102 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2900\nComponents:\n - input (input) params={\"shape\":[1,428]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,428]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":428,"outFeatures":578}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":578,"outFeatures":486}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":486,"outFeatures":249}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":249,"outFeatures":581}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":581,"outFeatures":487}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":487,"outFeatures":102}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1126596}} {"id":"gen-ae-2901","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1253-dim input: encode down to a 10-dim bottleneck and decode back to 1253. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2901\nComponents:\n - input (input) params={\"shape\":[1,1253]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1253]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1253,"outFeatures":376}},{"componentType":"linear","name":"enc2","params":{"inFeatures":376,"outFeatures":10}},{"componentType":"linear","name":"dec1","params":{"inFeatures":10,"outFeatures":376}},{"componentType":"linear","name":"dec2","params":{"inFeatures":376,"outFeatures":1253}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":949776}} {"id":"gen-cnn-2902","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 65 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2902\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":8125}} {"id":"gen-txf-2903","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 243-token sequences with 65 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2903\nComponents:\n - input (input) params={\"shape\":[1,243]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,243]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26550,"embeddingDim":176}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":176,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":4808144}} {"id":"gen-gqa-2904","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 480-token sequences with 82 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2904\nComponents:\n - input (input) params={\"shape\":[1,480]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,480]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40111,"embeddingDim":192}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":192,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[192]}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":7717056}} {"id":"gen-trim-2906","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2113-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2906\nComponents:\n - input (input) params={\"shape\":[1,181]}\n - fc1 (linear) params={\"inFeatures\":181,\"outFeatures\":2113}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2113,\"outFeatures\":2113}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2113,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":181,"outFeatures":267}},{"type":"update_params","name":"fc2","params":{"inFeatures":267,"outFeatures":267}},{"type":"update_params","name":"head","params":{"inFeatures":267,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":129495}} {"id":"gen-norm-2907","family":"norm","seed":20260716,"spec":"This 143-feature, 96-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2907\nComponents:\n - input (input) params={\"shape\":[1,143]}\n - fc1 (linear) params={\"inFeatures\":143,\"outFeatures\":386}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":386,\"outFeatures\":350}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":350,\"outFeatures\":96}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":386}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":350}}],"grade":{"pass":true,"score":94,"params":223898}} {"id":"gen-tower-2908","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 144-feature user input and a 109-feature item input, each through its own 2-layer MLP tower ending at width 318, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2908\nComponents:\n - user_input (input) params={\"shape\":[1,144]}\n - item_input (input) params={\"shape\":[1,109]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,144]}},{"componentType":"input","name":"item_input","params":{"shape":[1,109]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":144,"outFeatures":318}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":318,"outFeatures":318}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":109,"outFeatures":318}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":318,"outFeatures":318}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":636,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":283338}} {"id":"gen-grow-2909","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (109-feature input, 33 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2909\nComponents:\n - input (input) params={\"shape\":[1,109]}\n - fc1 (linear) params={\"inFeatures\":109,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":109,"outFeatures":643}},{"type":"update_params","name":"fc2","params":{"inFeatures":643,"outFeatures":643}},{"type":"update_params","name":"head","params":{"inFeatures":643,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":504755}} {"id":"gen-mlp-2910","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 61-feature input with 66 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2910\nComponents:\n - input (input) params={\"shape\":[1,61]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,61]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":61,"outFeatures":485}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":485,"outFeatures":619}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":619,"outFeatures":816}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":816,"outFeatures":31}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":66}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":862246}} {"id":"gen-ae-2911","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 314-dim input: encode down to a 143-dim bottleneck and decode back to 314. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2911\nComponents:\n - input (input) params={\"shape\":[1,314]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,314]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":314,"outFeatures":152}},{"componentType":"linear","name":"enc2","params":{"inFeatures":152,"outFeatures":143}},{"componentType":"linear","name":"dec1","params":{"inFeatures":143,"outFeatures":152}},{"componentType":"linear","name":"dec2","params":{"inFeatures":152,"outFeatures":314}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":138928}} {"id":"gen-cnn-2912","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x44x44 image with 87 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2912\nComponents:\n - input (input) params={\"shape\":[1,3,44,44]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,44,44]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":54,"outChannels":45,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":45,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":27243}} {"id":"gen-txf-2913","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 66-token sequences with 46 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2913\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,66]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34716,"embeddingDim":72}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":72,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":72,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":72,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":72,"outFeatures":46}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2565072}} {"id":"gen-gqa-2914","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 341-token sequences with 69 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2914\nComponents:\n - input (input) params={\"shape\":[1,341]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,341]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19129,"embeddingDim":736}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":736,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[736]}},{"componentType":"linear","name":"head","params":{"inFeatures":736,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":14129728}} {"id":"gen-fix-2915","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (428) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2915\nComponents:\n - input (input) params={\"shape\":[1,343]}\n - embed (embedding) params={\"numEmbeddings\":8943,\"embeddingDim\":428}\n - attn (multiHeadAttention) params={\"embedDim\":428,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":428,\"outFeatures\":81}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":4595008}} {"id":"gen-trim-2916","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2718-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2916\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - fc1 (linear) params={\"inFeatures\":123,\"outFeatures\":2718}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2718,\"outFeatures\":2718}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2718,\"outFeatures\":2718}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2718,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":123,"outFeatures":355}},{"type":"update_params","name":"fc2","params":{"inFeatures":355,"outFeatures":355}},{"type":"update_params","name":"fc3","params":{"inFeatures":355,"outFeatures":355}},{"type":"update_params","name":"head","params":{"inFeatures":355,"outFeatures":19}}],"grade":{"pass":true,"score":84,"params":302460}} {"id":"gen-norm-2917","family":"norm","seed":20260716,"spec":"This 83-feature, 7-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2917\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":360}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":360,\"outFeatures\":98}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":98,\"outFeatures\":437}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":437,\"outFeatures\":214}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":214,\"outFeatures\":254}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":254,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":360}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":98}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":437}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":214}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":254}}],"grade":{"pass":true,"score":100,"params":257638}} {"id":"gen-tower-2918","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 47-feature user input and a 214-feature item input, each through its own 2-layer MLP tower ending at width 39, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2918\nComponents:\n - user_input (input) params={\"shape\":[1,47]}\n - item_input (input) params={\"shape\":[1,214]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,47]}},{"componentType":"input","name":"item_input","params":{"shape":[1,214]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":47,"outFeatures":39}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":39,"outFeatures":39}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":214,"outFeatures":39}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":39,"outFeatures":39}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":78,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":13299}} {"id":"gen-grow-2919","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (59-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2919\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":59,"outFeatures":1026}},{"type":"update_params","name":"fc2","params":{"inFeatures":1026,"outFeatures":1026}},{"type":"update_params","name":"head","params":{"inFeatures":1026,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":1142964}} {"id":"gen-mlp-2920","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 175-feature input with 176 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2920\nComponents:\n - input (input) params={\"shape\":[1,175]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,175]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":175,"outFeatures":674}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":674,"outFeatures":560}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":560,"outFeatures":224}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":224,"outFeatures":686}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":686,"outFeatures":176}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":895230}} {"id":"gen-ae-2921","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 361-dim input: encode down to a 53-dim bottleneck and decode back to 361. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2921\nComponents:\n - input (input) params={\"shape\":[1,361]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,361]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":361,"outFeatures":514}},{"componentType":"linear","name":"enc2","params":{"inFeatures":514,"outFeatures":53}},{"componentType":"linear","name":"dec1","params":{"inFeatures":53,"outFeatures":514}},{"componentType":"linear","name":"dec2","params":{"inFeatures":514,"outFeatures":361}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":425592}} {"id":"gen-cnn-2922","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 79 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2922\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":16,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":16,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":14,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":39,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":26,"outFeatures":79}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":18542}} {"id":"gen-txf-2923","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 232-token sequences with 26 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2923\nComponents:\n - input (input) params={\"shape\":[1,232]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,232]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42163,"embeddingDim":120}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":120,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":120,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":5120280}} {"id":"gen-gqa-2924","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 297-token sequences with 25 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2924\nComponents:\n - input (input) params={\"shape\":[1,297]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,297]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":40009,"embeddingDim":608}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":608,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[608]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":608,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[608]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":608,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[608]}},{"componentType":"linear","name":"head","params":{"inFeatures":608,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":24340672}} {"id":"gen-trim-2926","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3257-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2926\nComponents:\n - input (input) params={\"shape\":[1,66]}\n - fc1 (linear) params={\"inFeatures\":66,\"outFeatures\":3257}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3257,\"outFeatures\":3257}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3257,\"outFeatures\":3257}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3257,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":66,"outFeatures":317}},{"type":"update_params","name":"fc2","params":{"inFeatures":317,"outFeatures":317}},{"type":"update_params","name":"fc3","params":{"inFeatures":317,"outFeatures":317}},{"type":"update_params","name":"head","params":{"inFeatures":317,"outFeatures":21}}],"grade":{"pass":true,"score":84,"params":228557}} {"id":"gen-norm-2927","family":"norm","seed":20260716,"spec":"This 212-feature, 38-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2927\nComponents:\n - input (input) params={\"shape\":[1,212]}\n - fc1 (linear) params={\"inFeatures\":212,\"outFeatures\":510}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":510,\"outFeatures\":45}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":45,\"outFeatures\":179}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":179,\"outFeatures\":38}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":510}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":45}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":179}}],"grade":{"pass":true,"score":100,"params":145927}} {"id":"gen-tower-2928","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 248-feature user input and a 154-feature item input, each through its own 2-layer MLP tower ending at width 349, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2928\nComponents:\n - user_input (input) params={\"shape\":[1,248]}\n - item_input (input) params={\"shape\":[1,154]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,248]}},{"componentType":"input","name":"item_input","params":{"shape":[1,154]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":248,"outFeatures":349}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":349,"outFeatures":349}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":154,"outFeatures":349}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":349,"outFeatures":349}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":698,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":384598}} {"id":"gen-grow-2929","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (62-feature input, 46 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2929\nComponents:\n - input (input) params={\"shape\":[1,62]}\n - fc1 (linear) params={\"inFeatures\":62,\"outFeatures\":13}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":13,\"outFeatures\":13}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":13,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":62,"outFeatures":851}},{"type":"update_params","name":"fc2","params":{"inFeatures":851,"outFeatures":851}},{"type":"update_params","name":"head","params":{"inFeatures":851,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":816109}} {"id":"gen-mlp-2930","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 422-feature input with 195 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2930\nComponents:\n - input (input) params={\"shape\":[1,422]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,422]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":422,"outFeatures":756}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":756,"outFeatures":112}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":112,"outFeatures":195}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":425544}} {"id":"gen-ae-2931","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 565-dim input: encode down to a 216-dim bottleneck and decode back to 565. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2931\nComponents:\n - input (input) params={\"shape\":[1,565]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,565]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":565,"outFeatures":810}},{"componentType":"linear","name":"enc2","params":{"inFeatures":810,"outFeatures":216}},{"componentType":"linear","name":"dec1","params":{"inFeatures":216,"outFeatures":810}},{"componentType":"linear","name":"dec2","params":{"inFeatures":810,"outFeatures":565}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1265220}} {"id":"gen-cnn-2932","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 89 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2932\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":15,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":15,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":21,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":89}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":7966}} {"id":"gen-txf-2933","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 326-token sequences with 28 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2933\nComponents:\n - input (input) params={\"shape\":[1,326]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,326]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":42849,"embeddingDim":232}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":232,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":232,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":232,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":10378056}} {"id":"gen-gqa-2934","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 402-token sequences with 16 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2934\nComponents:\n - input (input) params={\"shape\":[1,402]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,402]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20267,"embeddingDim":336}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[336]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":336,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[336]}},{"componentType":"linear","name":"head","params":{"inFeatures":336,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":6815088}} {"id":"gen-trim-2936","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2664-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2936\nComponents:\n - input (input) params={\"shape\":[1,114]}\n - fc1 (linear) params={\"inFeatures\":114,\"outFeatures\":2664}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2664,\"outFeatures\":2664}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2664,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":114,"outFeatures":169}},{"type":"update_params","name":"fc2","params":{"inFeatures":169,"outFeatures":169}},{"type":"update_params","name":"head","params":{"inFeatures":169,"outFeatures":33}}],"grade":{"pass":true,"score":80,"params":53404}} {"id":"gen-norm-2937","family":"norm","seed":20260716,"spec":"This 56-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2937\nComponents:\n - input (input) params={\"shape\":[1,56]}\n - fc1 (linear) params={\"inFeatures\":56,\"outFeatures\":56}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":56,\"outFeatures\":202}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":202,\"outFeatures\":384}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":384,\"outFeatures\":401}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":401,\"outFeatures\":480}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":480,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":56}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":202}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":384}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":401}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":480}}],"grade":{"pass":true,"score":100,"params":452880}} {"id":"gen-tower-2938","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 26-feature user input and a 20-feature item input, each through its own 2-layer MLP tower ending at width 335, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2938\nComponents:\n - user_input (input) params={\"shape\":[1,26]}\n - item_input (input) params={\"shape\":[1,20]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,26]}},{"componentType":"input","name":"item_input","params":{"shape":[1,20]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":26,"outFeatures":335}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":335,"outFeatures":335}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":20,"outFeatures":335}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":335,"outFeatures":335}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":670,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":240530}} {"id":"gen-grow-2939","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (27-feature input, 48 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2939\nComponents:\n - input (input) params={\"shape\":[1,27]}\n - fc1 (linear) params={\"inFeatures\":27,\"outFeatures\":7}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":7,\"outFeatures\":7}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":7,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":27,"outFeatures":783}},{"type":"update_params","name":"fc2","params":{"inFeatures":783,"outFeatures":783}},{"type":"update_params","name":"head","params":{"inFeatures":783,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":671814}} {"id":"gen-mlp-2940","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 113-feature input with 82 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2940\nComponents:\n - input (input) params={\"shape\":[1,113]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,113]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":113,"outFeatures":717}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":717,"outFeatures":888}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":888,"outFeatures":842}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":842,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":1534457}} {"id":"gen-ae-2941","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 579-dim input: encode down to a 116-dim bottleneck and decode back to 579. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2941\nComponents:\n - input (input) params={\"shape\":[1,579]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,579]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":579,"outFeatures":758}},{"componentType":"linear","name":"enc2","params":{"inFeatures":758,"outFeatures":116}},{"componentType":"linear","name":"dec1","params":{"inFeatures":116,"outFeatures":758}},{"componentType":"linear","name":"dec2","params":{"inFeatures":758,"outFeatures":579}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1053620}} {"id":"gen-cnn-2942","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 28 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2942\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":24,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":24,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":19,"outChannels":22,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":22,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29794}} {"id":"gen-txf-2943","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 370-token sequences with 58 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2943\nComponents:\n - input (input) params={\"shape\":[1,370]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,370]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14139,"embeddingDim":48}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":48,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":48,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":48,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":58}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":709104}} {"id":"gen-gqa-2944","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 96-token sequences with 3 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2944\nComponents:\n - input (input) params={\"shape\":[1,96]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,96]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17643,"embeddingDim":432}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":432,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[432]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":432,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[432]}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":3}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":7623072}} {"id":"gen-fix-2945","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (328) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2945\nComponents:\n - input (input) params={\"shape\":[1,243]}\n - embed (embedding) params={\"numEmbeddings\":9963,\"embeddingDim\":328}\n - attn (multiHeadAttention) params={\"embedDim\":328,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":328,\"outFeatures\":79}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":8}}],"grade":{"pass":true,"score":60,"params":3724112}} {"id":"gen-trim-2946","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3005-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2946\nComponents:\n - input (input) params={\"shape\":[1,137]}\n - fc1 (linear) params={\"inFeatures\":137,\"outFeatures\":3005}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3005,\"outFeatures\":3005}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3005,\"outFeatures\":3005}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3005,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":137,"outFeatures":319}},{"type":"update_params","name":"fc2","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"fc3","params":{"inFeatures":319,"outFeatures":319}},{"type":"update_params","name":"head","params":{"inFeatures":319,"outFeatures":47}}],"grade":{"pass":true,"score":84,"params":262218}} {"id":"gen-norm-2947","family":"norm","seed":20260716,"spec":"This 255-feature, 73-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2947\nComponents:\n - input (input) params={\"shape\":[1,255]}\n - fc1 (linear) params={\"inFeatures\":255,\"outFeatures\":284}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":284,\"outFeatures\":445}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":445,\"outFeatures\":73}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":284}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":445}}],"grade":{"pass":true,"score":94,"params":231285}} {"id":"gen-tower-2948","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 255-feature user input and a 38-feature item input, each through its own 2-layer MLP tower ending at width 86, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2948\nComponents:\n - user_input (input) params={\"shape\":[1,255]}\n - item_input (input) params={\"shape\":[1,38]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,255]}},{"componentType":"input","name":"item_input","params":{"shape":[1,38]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":255,"outFeatures":86}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":86,"outFeatures":86}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":38,"outFeatures":86}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":86,"outFeatures":86}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":40162}} {"id":"gen-grow-2949","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (28-feature input, 24 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2949\nComponents:\n - input (input) params={\"shape\":[1,28]}\n - fc1 (linear) params={\"inFeatures\":28,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":28,"outFeatures":858}},{"type":"update_params","name":"fc2","params":{"inFeatures":858,"outFeatures":858}},{"type":"update_params","name":"head","params":{"inFeatures":858,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":780780}} {"id":"gen-mlp-2950","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 324-feature input with 7 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2950\nComponents:\n - input (input) params={\"shape\":[1,324]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,324]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":324,"outFeatures":530}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":530,"outFeatures":995}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":995,"outFeatures":756}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":756,"outFeatures":544}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":544,"outFeatures":7}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":1866362}} {"id":"gen-ae-2951","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1595-dim input: encode down to a 165-dim bottleneck and decode back to 1595. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2951\nComponents:\n - input (input) params={\"shape\":[1,1595]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1595]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1595,"outFeatures":954}},{"componentType":"linear","name":"enc2","params":{"inFeatures":954,"outFeatures":165}},{"componentType":"linear","name":"dec1","params":{"inFeatures":165,"outFeatures":954}},{"componentType":"linear","name":"dec2","params":{"inFeatures":954,"outFeatures":1595}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3358080}} {"id":"gen-cnn-2952","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 69 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2952\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":47,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":14,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":51,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":17136}} {"id":"gen-txf-2953","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 228-token sequences with 59 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2953\nComponents:\n - input (input) params={\"shape\":[1,228]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,228]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":18837,"embeddingDim":148}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":148,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":148,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":148,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2971840}} {"id":"gen-gqa-2954","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 272-token sequences with 21 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2954\nComponents:\n - input (input) params={\"shape\":[1,272]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,272]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13225,"embeddingDim":192}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[192]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[192]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":192,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[192]}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":2543232}} {"id":"gen-fix-2955","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (306) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2955\nComponents:\n - input (input) params={\"shape\":[1,134]}\n - embed (embedding) params={\"numEmbeddings\":15871,\"embeddingDim\":306}\n - attn (multiHeadAttention) params={\"embedDim\":306,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":306,\"outFeatures\":72}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":5253102}} {"id":"gen-trim-2956","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 4011-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2956\nComponents:\n - input (input) params={\"shape\":[1,216]}\n - fc1 (linear) params={\"inFeatures\":216,\"outFeatures\":4011}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4011,\"outFeatures\":4011}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":4011,\"outFeatures\":4011}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":4011,\"outFeatures\":4011}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":4011,\"outFeatures\":12}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":216,"outFeatures":358}},{"type":"update_params","name":"fc2","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"fc3","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"fc4","params":{"inFeatures":358,"outFeatures":358}},{"type":"update_params","name":"head","params":{"inFeatures":358,"outFeatures":12}}],"grade":{"pass":true,"score":88,"params":466116}} {"id":"gen-norm-2957","family":"norm","seed":20260716,"spec":"This 72-feature, 78-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2957\nComponents:\n - input (input) params={\"shape\":[1,72]}\n - fc1 (linear) params={\"inFeatures\":72,\"outFeatures\":230}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":230,\"outFeatures\":322}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":322,\"outFeatures\":397}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":397,\"outFeatures\":136}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":136,\"outFeatures\":343}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":343,\"outFeatures\":78}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":230}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":322}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":397}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":136}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":343}}],"grade":{"pass":true,"score":100,"params":345848}} {"id":"gen-tower-2958","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 46-feature user input and a 182-feature item input, each through its own 2-layer MLP tower ending at width 62, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2958\nComponents:\n - user_input (input) params={\"shape\":[1,46]}\n - item_input (input) params={\"shape\":[1,182]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,46]}},{"componentType":"input","name":"item_input","params":{"shape":[1,182]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":46,"outFeatures":62}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":182,"outFeatures":62}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":21948}} {"id":"gen-grow-2959","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (50-feature input, 14 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2959\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":641}},{"type":"update_params","name":"fc2","params":{"inFeatures":641,"outFeatures":641}},{"type":"update_params","name":"head","params":{"inFeatures":641,"outFeatures":14}}],"grade":{"pass":true,"score":80,"params":451905}} {"id":"gen-mlp-2960","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 368-feature input with 57 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2960\nComponents:\n - input (input) params={\"shape\":[1,368]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,368]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":368,"outFeatures":915}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":915,"outFeatures":999}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":999,"outFeatures":243}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":243,"outFeatures":151}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":151,"outFeatures":905}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":905,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1718495}} {"id":"gen-ae-2961","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 649-dim input: encode down to a 176-dim bottleneck and decode back to 649. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2961\nComponents:\n - input (input) params={\"shape\":[1,649]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,649]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":649,"outFeatures":409}},{"componentType":"linear","name":"enc2","params":{"inFeatures":409,"outFeatures":176}},{"componentType":"linear","name":"dec1","params":{"inFeatures":176,"outFeatures":409}},{"componentType":"linear","name":"dec2","params":{"inFeatures":409,"outFeatures":649}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":674850}} {"id":"gen-cnn-2962","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x32x32 image with 27 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2962\nComponents:\n - input (input) params={\"shape\":[1,3,32,32]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,32,32]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":26,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":23,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":13,"outFeatures":27}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":24606}} {"id":"gen-txf-2963","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 141-token sequences with 81 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2963\nComponents:\n - input (input) params={\"shape\":[1,141]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,141]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17113,"embeddingDim":136}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":136,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":136,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":136,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":81}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":2560336}} {"id":"gen-gqa-2964","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 43-token sequences with 39 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2964\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,43]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38508,"embeddingDim":400}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[400]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[400]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":400,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[400]}},{"componentType":"linear","name":"head","params":{"inFeatures":400,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":15418800}} {"id":"gen-fix-2965","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (512) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2965\nComponents:\n - input (input) params={\"shape\":[1,128]}\n - embed (embedding) params={\"numEmbeddings\":12124,\"embeddingDim\":512}\n - attn (multiHeadAttention) params={\"embedDim\":512,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":512,\"outFeatures\":68}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":16}}],"grade":{"pass":true,"score":60,"params":7290880}} {"id":"gen-trim-2966","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3201-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2966\nComponents:\n - input (input) params={\"shape\":[1,138]}\n - fc1 (linear) params={\"inFeatures\":138,\"outFeatures\":3201}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3201,\"outFeatures\":3201}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3201,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":138,"outFeatures":124}},{"type":"update_params","name":"fc2","params":{"inFeatures":124,"outFeatures":124}},{"type":"update_params","name":"head","params":{"inFeatures":124,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":33852}} {"id":"gen-norm-2967","family":"norm","seed":20260716,"spec":"This 115-feature, 67-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2967\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - fc1 (linear) params={\"inFeatures\":115,\"outFeatures\":499}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":499,\"outFeatures\":119}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":119,\"outFeatures\":253}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":253,\"outFeatures\":216}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":216,\"outFeatures\":253}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":253,\"outFeatures\":67}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":499}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":119}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":253}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":216}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":253}}],"grade":{"pass":true,"score":100,"params":273120}} {"id":"gen-tower-2968","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 228-feature user input and a 76-feature item input, each through its own 2-layer MLP tower ending at width 448, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2968\nComponents:\n - user_input (input) params={\"shape\":[1,228]}\n - item_input (input) params={\"shape\":[1,76]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,228]}},{"componentType":"input","name":"item_input","params":{"shape":[1,76]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":228,"outFeatures":448}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":76,"outFeatures":448}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":448,"outFeatures":448}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":896,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":538496}} {"id":"gen-grow-2969","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (68-feature input, 8 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2969\nComponents:\n - input (input) params={\"shape\":[1,68]}\n - fc1 (linear) params={\"inFeatures\":68,\"outFeatures\":15}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":15,\"outFeatures\":15}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":15,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":68,"outFeatures":801}},{"type":"update_params","name":"fc2","params":{"inFeatures":801,"outFeatures":801}},{"type":"update_params","name":"head","params":{"inFeatures":801,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":702477}} {"id":"gen-mlp-2970","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 330-feature input with 57 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2970\nComponents:\n - input (input) params={\"shape\":[1,330]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,330]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":330,"outFeatures":685}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":685,"outFeatures":31}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":31,"outFeatures":765}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":765,"outFeatures":529}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":529,"outFeatures":906}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":906,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1206601}} {"id":"gen-ae-2971","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 883-dim input: encode down to a 19-dim bottleneck and decode back to 883. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2971\nComponents:\n - input (input) params={\"shape\":[1,883]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,883]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":883,"outFeatures":883}},{"componentType":"linear","name":"enc2","params":{"inFeatures":883,"outFeatures":19}},{"componentType":"linear","name":"dec1","params":{"inFeatures":19,"outFeatures":883}},{"componentType":"linear","name":"dec2","params":{"inFeatures":883,"outFeatures":883}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1592932}} {"id":"gen-cnn-2972","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x37x37 image with 69 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2972\nComponents:\n - input (input) params={\"shape\":[1,3,37,37]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,37,37]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":59,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":59,"outChannels":19,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":19,"outFeatures":69}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":16980}} {"id":"gen-txf-2973","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 332-token sequences with 30 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2973\nComponents:\n - input (input) params={\"shape\":[1,332]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,332]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41657,"embeddingDim":208}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":208,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":208,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":208,"outFeatures":30}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":9017008}} {"id":"gen-gqa-2974","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 459-token sequences with 8 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2974\nComponents:\n - input (input) params={\"shape\":[1,459]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,459]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":45871,"embeddingDim":136}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":136,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[136]}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":8}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":6239544}} {"id":"gen-trim-2976","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3359-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2976\nComponents:\n - input (input) params={\"shape\":[1,254]}\n - fc1 (linear) params={\"inFeatures\":254,\"outFeatures\":3359}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3359,\"outFeatures\":3359}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3359,\"outFeatures\":3359}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3359,\"outFeatures\":26}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":254,"outFeatures":210}},{"type":"update_params","name":"fc2","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"fc3","params":{"inFeatures":210,"outFeatures":210}},{"type":"update_params","name":"head","params":{"inFeatures":210,"outFeatures":26}}],"grade":{"pass":true,"score":84,"params":147000}} {"id":"gen-norm-2977","family":"norm","seed":20260716,"spec":"This 201-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2977\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - fc1 (linear) params={\"inFeatures\":201,\"outFeatures\":279}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":279,\"outFeatures\":490}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":490,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":279}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":490}}],"grade":{"pass":true,"score":94,"params":207489}} {"id":"gen-tower-2978","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 138-feature user input and a 170-feature item input, each through its own 2-layer MLP tower ending at width 368, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2978\nComponents:\n - user_input (input) params={\"shape\":[1,138]}\n - item_input (input) params={\"shape\":[1,170]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,138]}},{"componentType":"input","name":"item_input","params":{"shape":[1,170]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":138,"outFeatures":368}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":368,"outFeatures":368}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":170,"outFeatures":368}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":368,"outFeatures":368}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":736,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":384928}} {"id":"gen-grow-2979","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (82-feature input, 42 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2979\nComponents:\n - input (input) params={\"shape\":[1,82]}\n - fc1 (linear) params={\"inFeatures\":82,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":82,"outFeatures":870}},{"type":"update_params","name":"fc2","params":{"inFeatures":870,"outFeatures":870}},{"type":"update_params","name":"head","params":{"inFeatures":870,"outFeatures":42}}],"grade":{"pass":true,"score":80,"params":864780}} {"id":"gen-mlp-2980","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 276-feature input with 59 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2980\nComponents:\n - input (input) params={\"shape\":[1,276]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,276]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":276,"outFeatures":681}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":681,"outFeatures":601}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":601,"outFeatures":576}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":576,"outFeatures":82}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":82,"outFeatures":715}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":715,"outFeatures":133}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":133,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1152217}} {"id":"gen-ae-2981","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 714-dim input: encode down to a 191-dim bottleneck and decode back to 714. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2981\nComponents:\n - input (input) params={\"shape\":[1,714]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,714]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":714,"outFeatures":1017}},{"componentType":"linear","name":"enc2","params":{"inFeatures":1017,"outFeatures":191}},{"componentType":"linear","name":"dec1","params":{"inFeatures":191,"outFeatures":1017}},{"componentType":"linear","name":"dec2","params":{"inFeatures":1017,"outFeatures":714}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1840770}} {"id":"gen-cnn-2982","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x34x34 image with 83 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2982\nComponents:\n - input (input) params={\"shape\":[1,3,34,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,34,34]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":21,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":21,"outFeatures":83}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":3903}} {"id":"gen-txf-2983","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 301-token sequences with 67 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2983\nComponents:\n - input (input) params={\"shape\":[1,301]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,301]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49359,"embeddingDim":136}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":136,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":136,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":6795920}} {"id":"gen-gqa-2984","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 491-token sequences with 60 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2984\nComponents:\n - input (input) params={\"shape\":[1,491]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,491]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":33165,"embeddingDim":124}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":124,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[124]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":124,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[124]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":124,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[124]}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":60}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":4119900}} {"id":"gen-trim-2986","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3714-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2986\nComponents:\n - input (input) params={\"shape\":[1,149]}\n - fc1 (linear) params={\"inFeatures\":149,\"outFeatures\":3714}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3714,\"outFeatures\":3714}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3714,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":149,"outFeatures":316}},{"type":"update_params","name":"fc2","params":{"inFeatures":316,"outFeatures":316}},{"type":"update_params","name":"head","params":{"inFeatures":316,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":158316}} {"id":"gen-norm-2987","family":"norm","seed":20260716,"spec":"This 175-feature, 8-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2987\nComponents:\n - input (input) params={\"shape\":[1,175]}\n - fc1 (linear) params={\"inFeatures\":175,\"outFeatures\":346}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":346,\"outFeatures\":439}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":439,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":346}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":439}}],"grade":{"pass":true,"score":94,"params":215956}} {"id":"gen-tower-2988","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 112-feature user input and a 93-feature item input, each through its own 2-layer MLP tower ending at width 280, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2988\nComponents:\n - user_input (input) params={\"shape\":[1,112]}\n - item_input (input) params={\"shape\":[1,93]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,112]}},{"componentType":"input","name":"item_input","params":{"shape":[1,93]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":112,"outFeatures":280}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":280,"outFeatures":280}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":93,"outFeatures":280}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":280,"outFeatures":280}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":214760}} {"id":"gen-grow-2989","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (38-feature input, 5 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2989\nComponents:\n - input (input) params={\"shape\":[1,38]}\n - fc1 (linear) params={\"inFeatures\":38,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":38,"outFeatures":646}},{"type":"update_params","name":"fc2","params":{"inFeatures":646,"outFeatures":646}},{"type":"update_params","name":"head","params":{"inFeatures":646,"outFeatures":5}}],"grade":{"pass":true,"score":80,"params":445094}} {"id":"gen-mlp-2990","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 13-feature input with 31 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-2990\nComponents:\n - input (input) params={\"shape\":[1,13]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,13]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":13,"outFeatures":661}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":661,"outFeatures":386}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":386,"outFeatures":31}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":275705}} {"id":"gen-ae-2991","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 945-dim input: encode down to a 227-dim bottleneck and decode back to 945. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-2991\nComponents:\n - input (input) params={\"shape\":[1,945]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,945]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":945,"outFeatures":359}},{"componentType":"linear","name":"enc2","params":{"inFeatures":359,"outFeatures":227}},{"componentType":"linear","name":"dec1","params":{"inFeatures":227,"outFeatures":359}},{"componentType":"linear","name":"dec2","params":{"inFeatures":359,"outFeatures":945}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":841496}} {"id":"gen-cnn-2992","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x64x64 image with 50 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-2992\nComponents:\n - input (input) params={\"shape\":[1,3,64,64]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,64,64]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":26,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":26,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":13,"outFeatures":50}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":4394}} {"id":"gen-txf-2993","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 300-token sequences with 49 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-2993\nComponents:\n - input (input) params={\"shape\":[1,300]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,300]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17089,"embeddingDim":160}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":160,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":160,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2844480}} {"id":"gen-gqa-2994","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 282-token sequences with 78 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-2994\nComponents:\n - input (input) params={\"shape\":[1,282]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,282]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37953,"embeddingDim":80}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":80,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[80]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":80,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[80]}},{"componentType":"linear","name":"head","params":{"inFeatures":80,"outFeatures":78}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3042480}} {"id":"gen-fix-2995","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (366) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-2995\nComponents:\n - input (input) params={\"shape\":[1,123]}\n - embed (embedding) params={\"numEmbeddings\":44954,\"embeddingDim\":366}\n - attn (multiHeadAttention) params={\"embedDim\":366,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":366,\"outFeatures\":70}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":17014608}} {"id":"gen-trim-2996","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2322-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-2996\nComponents:\n - input (input) params={\"shape\":[1,133]}\n - fc1 (linear) params={\"inFeatures\":133,\"outFeatures\":2322}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2322,\"outFeatures\":2322}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2322,\"outFeatures\":2322}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2322,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":133,"outFeatures":223}},{"type":"update_params","name":"fc2","params":{"inFeatures":223,"outFeatures":223}},{"type":"update_params","name":"fc3","params":{"inFeatures":223,"outFeatures":223}},{"type":"update_params","name":"head","params":{"inFeatures":223,"outFeatures":16}}],"grade":{"pass":true,"score":84,"params":132685}} {"id":"gen-norm-2997","family":"norm","seed":20260716,"spec":"This 215-feature, 91-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-2997\nComponents:\n - input (input) params={\"shape\":[1,215]}\n - fc1 (linear) params={\"inFeatures\":215,\"outFeatures\":278}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":278,\"outFeatures\":89}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":89,\"outFeatures\":55}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":55,\"outFeatures\":135}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":135,\"outFeatures\":410}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":410,\"outFeatures\":91}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":278}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":89}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":55}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":135}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":410}}],"grade":{"pass":true,"score":100,"params":189492}} {"id":"gen-tower-2998","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 201-feature user input and a 42-feature item input, each through its own 2-layer MLP tower ending at width 460, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-2998\nComponents:\n - user_input (input) params={\"shape\":[1,201]}\n - item_input (input) params={\"shape\":[1,42]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,201]}},{"componentType":"input","name":"item_input","params":{"shape":[1,42]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":201,"outFeatures":460}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":460,"outFeatures":460}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":42,"outFeatures":460}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":460,"outFeatures":460}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":920,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":535900}} {"id":"gen-grow-2999","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (63-feature input, 47 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-2999\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - fc1 (linear) params={\"inFeatures\":63,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":63,"outFeatures":1167}},{"type":"update_params","name":"fc2","params":{"inFeatures":1167,"outFeatures":1167}},{"type":"update_params","name":"head","params":{"inFeatures":1167,"outFeatures":47}}],"grade":{"pass":true,"score":80,"params":1490259}} {"id":"gen-mlp-3000","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 449-feature input with 97 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3000\nComponents:\n - input (input) params={\"shape\":[1,449]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,449]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":449,"outFeatures":383}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":383,"outFeatures":630}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":630,"outFeatures":97}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":474367}} {"id":"gen-ae-3001","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 822-dim input: encode down to a 172-dim bottleneck and decode back to 822. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3001\nComponents:\n - input (input) params={\"shape\":[1,822]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,822]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":822,"outFeatures":501}},{"componentType":"linear","name":"enc2","params":{"inFeatures":501,"outFeatures":172}},{"componentType":"linear","name":"dec1","params":{"inFeatures":172,"outFeatures":501}},{"componentType":"linear","name":"dec2","params":{"inFeatures":501,"outFeatures":822}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":995988}} {"id":"gen-cnn-3002","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x50x50 image with 33 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3002\nComponents:\n - input (input) params={\"shape\":[1,3,50,50]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,50,50]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":29,"outChannels":52,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":52,"outFeatures":33}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":19320}} {"id":"gen-txf-3003","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 99-token sequences with 39 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3003\nComponents:\n - input (input) params={\"shape\":[1,99]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,99]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":39104,"embeddingDim":88}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":88,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":88,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":88,"outFeatures":39}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3537512}} {"id":"gen-gqa-3004","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 238-token sequences with 92 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3004\nComponents:\n - input (input) params={\"shape\":[1,238]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,238]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47186,"embeddingDim":368}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":368,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[368]}},{"componentType":"linear","name":"head","params":{"inFeatures":368,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":17398304}} {"id":"gen-fix-3005","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (484) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3005\nComponents:\n - input (input) params={\"shape\":[1,204]}\n - embed (embedding) params={\"numEmbeddings\":44075,\"embeddingDim\":484}\n - attn (multiHeadAttention) params={\"embedDim\":484,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":484,\"outFeatures\":92}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":22313852}} {"id":"gen-trim-3006","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 1595-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3006\nComponents:\n - input (input) params={\"shape\":[1,50]}\n - fc1 (linear) params={\"inFeatures\":50,\"outFeatures\":1595}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1595,\"outFeatures\":1595}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1595,\"outFeatures\":1595}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":1595,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":50,"outFeatures":68}},{"type":"update_params","name":"fc2","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"fc3","params":{"inFeatures":68,"outFeatures":68}},{"type":"update_params","name":"head","params":{"inFeatures":68,"outFeatures":18}}],"grade":{"pass":true,"score":84,"params":13872}} {"id":"gen-norm-3007","family":"norm","seed":20260716,"spec":"This 210-feature, 24-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3007\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - fc1 (linear) params={\"inFeatures\":210,\"outFeatures\":491}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":491,\"outFeatures\":271}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":271,\"outFeatures\":165}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":165,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":491}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":271}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":165}}],"grade":{"pass":true,"score":100,"params":284846}} {"id":"gen-tower-3008","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 19-feature user input and a 45-feature item input, each through its own 2-layer MLP tower ending at width 160, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3008\nComponents:\n - user_input (input) params={\"shape\":[1,19]}\n - item_input (input) params={\"shape\":[1,45]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,19]}},{"componentType":"input","name":"item_input","params":{"shape":[1,45]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":19,"outFeatures":160}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":160,"outFeatures":160}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":45,"outFeatures":160}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":160,"outFeatures":160}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":320,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":61760}} {"id":"gen-grow-3009","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (95-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3009\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":95,"outFeatures":1377}},{"type":"update_params","name":"fc2","params":{"inFeatures":1377,"outFeatures":1377}},{"type":"update_params","name":"head","params":{"inFeatures":1377,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":2036583}} {"id":"gen-mlp-3010","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 193-feature input with 28 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3010\nComponents:\n - input (input) params={\"shape\":[1,193]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,193]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":193,"outFeatures":839}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":839,"outFeatures":648}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":648,"outFeatures":28}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":723743}} {"id":"gen-ae-3011","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1333-dim input: encode down to a 131-dim bottleneck and decode back to 1333. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3011\nComponents:\n - input (input) params={\"shape\":[1,1333]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1333]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1333,"outFeatures":372}},{"componentType":"linear","name":"enc2","params":{"inFeatures":372,"outFeatures":131}},{"componentType":"linear","name":"dec1","params":{"inFeatures":131,"outFeatures":372}},{"componentType":"linear","name":"dec2","params":{"inFeatures":372,"outFeatures":1333}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1089216}} {"id":"gen-cnn-3012","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x31x31 image with 91 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3012\nComponents:\n - input (input) params={\"shape\":[1,3,31,31]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,31,31]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":9,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":10,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":18,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":54,"outFeatures":91}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":16335}} {"id":"gen-txf-3013","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 491-token sequences with 32 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3013\nComponents:\n - input (input) params={\"shape\":[1,491]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,491]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":41436,"embeddingDim":76}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":76,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":76,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":76,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":76,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3220880}} {"id":"gen-gqa-3014","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 512-token sequences with 32 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3014\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":19801,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3490608}} {"id":"gen-trim-3016","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2368-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3016\nComponents:\n - input (input) params={\"shape\":[1,206]}\n - fc1 (linear) params={\"inFeatures\":206,\"outFeatures\":2368}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2368,\"outFeatures\":2368}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2368,\"outFeatures\":2368}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2368,\"outFeatures\":2368}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2368,\"outFeatures\":50}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":206,"outFeatures":129}},{"type":"update_params","name":"fc2","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"fc3","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"fc4","params":{"inFeatures":129,"outFeatures":129}},{"type":"update_params","name":"head","params":{"inFeatures":129,"outFeatures":50}}],"grade":{"pass":true,"score":88,"params":82947}} {"id":"gen-norm-3017","family":"norm","seed":20260716,"spec":"This 46-feature, 59-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3017\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":232}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":232,\"outFeatures\":451}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":451,\"outFeatures\":511}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":511,\"outFeatures\":469}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":469,\"outFeatures\":59}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":232}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":511}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":469}}],"grade":{"pass":true,"score":100,"params":613095}} {"id":"gen-tower-3018","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 55-feature user input and a 82-feature item input, each through its own 2-layer MLP tower ending at width 53, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3018\nComponents:\n - user_input (input) params={\"shape\":[1,55]}\n - item_input (input) params={\"shape\":[1,82]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,55]}},{"componentType":"input","name":"item_input","params":{"shape":[1,82]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":55,"outFeatures":53}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":53,"outFeatures":53}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":82,"outFeatures":53}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":53,"outFeatures":53}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":106,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":12985}} {"id":"gen-grow-3019","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (84-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3019\nComponents:\n - input (input) params={\"shape\":[1,84]}\n - fc1 (linear) params={\"inFeatures\":84,\"outFeatures\":14}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":14,\"outFeatures\":14}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":14,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":84,"outFeatures":696}},{"type":"update_params","name":"fc2","params":{"inFeatures":696,"outFeatures":696}},{"type":"update_params","name":"head","params":{"inFeatures":696,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":568632}} {"id":"gen-mlp-3020","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 413-feature input with 172 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3020\nComponents:\n - input (input) params={\"shape\":[1,413]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,413]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":413,"outFeatures":435}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":435,"outFeatures":508}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":508,"outFeatures":202}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":202,"outFeatures":666}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":666,"outFeatures":172}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":752335}} {"id":"gen-ae-3021","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1040-dim input: encode down to a 90-dim bottleneck and decode back to 1040. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3021\nComponents:\n - input (input) params={\"shape\":[1,1040]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1040]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1040,"outFeatures":97}},{"componentType":"linear","name":"enc2","params":{"inFeatures":97,"outFeatures":90}},{"componentType":"linear","name":"dec1","params":{"inFeatures":90,"outFeatures":97}},{"componentType":"linear","name":"dec2","params":{"inFeatures":97,"outFeatures":1040}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":219220}} {"id":"gen-cnn-3022","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 29 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3022\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":40,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":27,"outChannels":29,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":29,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":18688}} {"id":"gen-txf-3023","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 157-token sequences with 56 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3023\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,157]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":27863,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":104,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":3033368}} {"id":"gen-gqa-3024","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 419-token sequences with 65 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3024\nComponents:\n - input (input) params={\"shape\":[1,419]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,419]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22482,"embeddingDim":172}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[172]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":172,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[172]}},{"componentType":"linear","name":"head","params":{"inFeatures":172,"outFeatures":65}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3878084}} {"id":"gen-fix-3025","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (300) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3025\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - embed (embedding) params={\"numEmbeddings\":31769,\"embeddingDim\":300}\n - attn (multiHeadAttention) params={\"embedDim\":300,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":300,\"outFeatures\":14}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":9894900}} {"id":"gen-trim-3026","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3021-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3026\nComponents:\n - input (input) params={\"shape\":[1,256]}\n - fc1 (linear) params={\"inFeatures\":256,\"outFeatures\":3021}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3021,\"outFeatures\":3021}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3021,\"outFeatures\":3021}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3021,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":256,"outFeatures":163}},{"type":"update_params","name":"fc2","params":{"inFeatures":163,"outFeatures":163}},{"type":"update_params","name":"fc3","params":{"inFeatures":163,"outFeatures":163}},{"type":"update_params","name":"head","params":{"inFeatures":163,"outFeatures":40}}],"grade":{"pass":true,"score":84,"params":101386}} {"id":"gen-norm-3027","family":"norm","seed":20260716,"spec":"This 209-feature, 55-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3027\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - fc1 (linear) params={\"inFeatures\":209,\"outFeatures\":365}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":365,\"outFeatures\":162}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":162,\"outFeatures\":360}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":360,\"outFeatures\":482}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":482,\"outFeatures\":55}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":365}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":162}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":360}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":482}}],"grade":{"pass":true,"score":100,"params":393765}} {"id":"gen-tower-3028","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 205-feature user input and a 252-feature item input, each through its own 2-layer MLP tower ending at width 288, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3028\nComponents:\n - user_input (input) params={\"shape\":[1,205]}\n - item_input (input) params={\"shape\":[1,252]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,205]}},{"componentType":"input","name":"item_input","params":{"shape":[1,252]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":205,"outFeatures":288}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":252,"outFeatures":288}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":288,"outFeatures":288}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":576,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":298080}} {"id":"gen-grow-3029","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (110-feature input, 29 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3029\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":110,"outFeatures":957}},{"type":"update_params","name":"fc2","params":{"inFeatures":957,"outFeatures":957}},{"type":"update_params","name":"head","params":{"inFeatures":957,"outFeatures":29}}],"grade":{"pass":true,"score":80,"params":1048872}} {"id":"gen-mlp-3030","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 251-feature input with 21 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3030\nComponents:\n - input (input) params={\"shape\":[1,251]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,251]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":251,"outFeatures":901}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":901,"outFeatures":132}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":132,"outFeatures":211}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":211,"outFeatures":329}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":329,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":449263}} {"id":"gen-ae-3031","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1573-dim input: encode down to a 164-dim bottleneck and decode back to 1573. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3031\nComponents:\n - input (input) params={\"shape\":[1,1573]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1573]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1573,"outFeatures":313}},{"componentType":"linear","name":"enc2","params":{"inFeatures":313,"outFeatures":164}},{"componentType":"linear","name":"dec1","params":{"inFeatures":164,"outFeatures":313}},{"componentType":"linear","name":"dec2","params":{"inFeatures":313,"outFeatures":1573}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1087362}} {"id":"gen-cnn-3032","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x33x33 image with 11 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3032\nComponents:\n - input (input) params={\"shape\":[1,3,33,33]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,33,33]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":39,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":39,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":57,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":42,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":58166}} {"id":"gen-txf-3033","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 293-token sequences with 26 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3033\nComponents:\n - input (input) params={\"shape\":[1,293]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,293]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":15562,"embeddingDim":144}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":144,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":26}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2327616}} {"id":"gen-gqa-3034","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 340-token sequences with 21 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3034\nComponents:\n - input (input) params={\"shape\":[1,340]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,340]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":16556,"embeddingDim":176}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[176]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":176,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[176]}},{"componentType":"linear","name":"head","params":{"inFeatures":176,"outFeatures":21}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2917552}} {"id":"gen-trim-3036","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 1874-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3036\nComponents:\n - input (input) params={\"shape\":[1,172]}\n - fc1 (linear) params={\"inFeatures\":172,\"outFeatures\":1874}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1874,\"outFeatures\":1874}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":1874,\"outFeatures\":8}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":172,"outFeatures":236}},{"type":"update_params","name":"fc2","params":{"inFeatures":236,"outFeatures":236}},{"type":"update_params","name":"head","params":{"inFeatures":236,"outFeatures":8}}],"grade":{"pass":true,"score":80,"params":98176}} {"id":"gen-norm-3037","family":"norm","seed":20260716,"spec":"This 70-feature, 13-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3037\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - fc1 (linear) params={\"inFeatures\":70,\"outFeatures\":280}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":280,\"outFeatures\":111}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":111,\"outFeatures\":302}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":302,\"outFeatures\":13}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":280}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":111}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":302}}],"grade":{"pass":true,"score":100,"params":88128}} {"id":"gen-tower-3038","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 110-feature user input and a 124-feature item input, each through its own 2-layer MLP tower ending at width 217, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3038\nComponents:\n - user_input (input) params={\"shape\":[1,110]}\n - item_input (input) params={\"shape\":[1,124]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,110]}},{"componentType":"input","name":"item_input","params":{"shape":[1,124]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":110,"outFeatures":217}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":217,"outFeatures":217}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":124,"outFeatures":217}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":217,"outFeatures":217}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":434,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":145390}} {"id":"gen-grow-3039","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (51-feature input, 21 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3039\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":17}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":17,\"outFeatures\":17}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":17,\"outFeatures\":21}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":1022}},{"type":"update_params","name":"fc2","params":{"inFeatures":1022,"outFeatures":1022}},{"type":"update_params","name":"head","params":{"inFeatures":1022,"outFeatures":21}}],"grade":{"pass":true,"score":80,"params":1118068}} {"id":"gen-mlp-3040","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 157-feature input with 128 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3040\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,157]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":157,"outFeatures":346}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":346,"outFeatures":451}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":451,"outFeatures":675}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":675,"outFeatures":128}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":601193}} {"id":"gen-ae-3041","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 339-dim input: encode down to a 106-dim bottleneck and decode back to 339. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3041\nComponents:\n - input (input) params={\"shape\":[1,339]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,339]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":339,"outFeatures":554}},{"componentType":"linear","name":"enc2","params":{"inFeatures":554,"outFeatures":106}},{"componentType":"linear","name":"dec1","params":{"inFeatures":106,"outFeatures":554}},{"componentType":"linear","name":"dec2","params":{"inFeatures":554,"outFeatures":339}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":493060}} {"id":"gen-cnn-3042","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 34 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3042\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":55,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":57,"outChannels":27,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":27,"outFeatures":34}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":44469}} {"id":"gen-txf-3043","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 271-token sequences with 29 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3043\nComponents:\n - input (input) params={\"shape\":[1,271]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,271]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":44125,"embeddingDim":220}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":220,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":220,"outFeatures":29}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":9907480}} {"id":"gen-gqa-3044","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 105-token sequences with 94 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3044\nComponents:\n - input (input) params={\"shape\":[1,105]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,105]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":25427,"embeddingDim":768}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":768,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[768]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":768,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[768]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":768,"numHeads":16,"numKVHeads":8}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[768]}},{"componentType":"linear","name":"head","params":{"inFeatures":768,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":19600128}} {"id":"gen-trim-3046","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3763-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3046\nComponents:\n - input (input) params={\"shape\":[1,167]}\n - fc1 (linear) params={\"inFeatures\":167,\"outFeatures\":3763}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3763,\"outFeatures\":3763}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3763,\"outFeatures\":3763}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3763,\"outFeatures\":3763}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3763,\"outFeatures\":44}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":167,"outFeatures":186}},{"type":"update_params","name":"fc2","params":{"inFeatures":186,"outFeatures":186}},{"type":"update_params","name":"fc3","params":{"inFeatures":186,"outFeatures":186}},{"type":"update_params","name":"fc4","params":{"inFeatures":186,"outFeatures":186}},{"type":"update_params","name":"head","params":{"inFeatures":186,"outFeatures":44}}],"grade":{"pass":true,"score":88,"params":143034}} {"id":"gen-norm-3047","family":"norm","seed":20260716,"spec":"This 22-feature, 59-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3047\nComponents:\n - input (input) params={\"shape\":[1,22]}\n - fc1 (linear) params={\"inFeatures\":22,\"outFeatures\":69}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":69,\"outFeatures\":218}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":218,\"outFeatures\":332}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":332,\"outFeatures\":145}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":145,\"outFeatures\":497}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":497,\"outFeatures\":59}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":69}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":218}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":332}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":145}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":497}}],"grade":{"pass":true,"score":100,"params":238464}} {"id":"gen-tower-3048","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 119-feature user input and a 52-feature item input, each through its own 2-layer MLP tower ending at width 42, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3048\nComponents:\n - user_input (input) params={\"shape\":[1,119]}\n - item_input (input) params={\"shape\":[1,52]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,119]}},{"componentType":"input","name":"item_input","params":{"shape":[1,52]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":119,"outFeatures":42}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":42,"outFeatures":42}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":52,"outFeatures":42}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":42,"outFeatures":42}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":84,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":10794}} {"id":"gen-grow-3049","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (94-feature input, 4 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3049\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - fc1 (linear) params={\"inFeatures\":94,\"outFeatures\":19}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":19,\"outFeatures\":19}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":19,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":94,"outFeatures":1022}},{"type":"update_params","name":"fc2","params":{"inFeatures":1022,"outFeatures":1022}},{"type":"update_params","name":"head","params":{"inFeatures":1022,"outFeatures":4}}],"grade":{"pass":true,"score":80,"params":1144640}} {"id":"gen-mlp-3050","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 209-feature input with 149 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3050\nComponents:\n - input (input) params={\"shape\":[1,209]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,209]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":209,"outFeatures":184}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":184,"outFeatures":953}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":953,"outFeatures":149}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":355805}} {"id":"gen-ae-3051","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1958-dim input: encode down to a 206-dim bottleneck and decode back to 1958. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3051\nComponents:\n - input (input) params={\"shape\":[1,1958]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1958]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1958,"outFeatures":564}},{"componentType":"linear","name":"enc2","params":{"inFeatures":564,"outFeatures":206}},{"componentType":"linear","name":"dec1","params":{"inFeatures":206,"outFeatures":564}},{"componentType":"linear","name":"dec2","params":{"inFeatures":564,"outFeatures":1958}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2440992}} {"id":"gen-cnn-3052","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x63x63 image with 75 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3052\nComponents:\n - input (input) params={\"shape\":[1,3,63,63]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,63,63]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":55,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":55,"outChannels":44,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":44,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":47,"outChannels":18,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":18,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":50841}} {"id":"gen-txf-3053","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 398-token sequences with 41 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3053\nComponents:\n - input (input) params={\"shape\":[1,398]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,398]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10950,"embeddingDim":32}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":32,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":32,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":32,"outFeatures":41}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":359904}} {"id":"gen-gqa-3054","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 344-token sequences with 92 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3054\nComponents:\n - input (input) params={\"shape\":[1,344]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,344]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":14430,"embeddingDim":92}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[92]}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":92}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1336024}} {"id":"gen-trim-3056","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3523-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3056\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - fc1 (linear) params={\"inFeatures\":225,\"outFeatures\":3523}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3523,\"outFeatures\":3523}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3523,\"outFeatures\":3523}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3523,\"outFeatures\":3523}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3523,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":225,"outFeatures":346}},{"type":"update_params","name":"fc2","params":{"inFeatures":346,"outFeatures":346}},{"type":"update_params","name":"fc3","params":{"inFeatures":346,"outFeatures":346}},{"type":"update_params","name":"fc4","params":{"inFeatures":346,"outFeatures":346}},{"type":"update_params","name":"head","params":{"inFeatures":346,"outFeatures":19}}],"grade":{"pass":true,"score":88,"params":443572}} {"id":"gen-norm-3057","family":"norm","seed":20260716,"spec":"This 220-feature, 32-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3057\nComponents:\n - input (input) params={\"shape\":[1,220]}\n - fc1 (linear) params={\"inFeatures\":220,\"outFeatures\":396}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":396,\"outFeatures\":217}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":217,\"outFeatures\":473}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":473,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":396}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":217}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":473}}],"grade":{"pass":true,"score":100,"params":290829}} {"id":"gen-tower-3058","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 222-feature user input and a 77-feature item input, each through its own 2-layer MLP tower ending at width 151, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3058\nComponents:\n - user_input (input) params={\"shape\":[1,222]}\n - item_input (input) params={\"shape\":[1,77]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,222]}},{"componentType":"input","name":"item_input","params":{"shape":[1,77]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":222,"outFeatures":151}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":77,"outFeatures":151}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":151,"outFeatures":151}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":302,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":91053}} {"id":"gen-grow-3059","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (46-feature input, 45 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3059\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":673}},{"type":"update_params","name":"fc2","params":{"inFeatures":673,"outFeatures":673}},{"type":"update_params","name":"head","params":{"inFeatures":673,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":514172}} {"id":"gen-mlp-3060","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 167-feature input with 132 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3060\nComponents:\n - input (input) params={\"shape\":[1,167]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,167]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":167,"outFeatures":879}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":879,"outFeatures":484}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":484,"outFeatures":194}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":194,"outFeatures":451}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":451,"outFeatures":269}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":269,"outFeatures":25}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":25,"outFeatures":132}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":884963}} {"id":"gen-ae-3061","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 679-dim input: encode down to a 71-dim bottleneck and decode back to 679. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3061\nComponents:\n - input (input) params={\"shape\":[1,679]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,679]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":679,"outFeatures":329}},{"componentType":"linear","name":"enc2","params":{"inFeatures":329,"outFeatures":71}},{"componentType":"linear","name":"dec1","params":{"inFeatures":71,"outFeatures":329}},{"componentType":"linear","name":"dec2","params":{"inFeatures":329,"outFeatures":679}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":493500}} {"id":"gen-cnn-3062","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 55 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3062\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":57,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":57,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":41,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":62,"outFeatures":55}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":48860}} {"id":"gen-txf-3063","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 470-token sequences with 42 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3063\nComponents:\n - input (input) params={\"shape\":[1,470]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,470]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24690,"embeddingDim":416}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":416,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":416,"outFeatures":42}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":10980736}} {"id":"gen-gqa-3064","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 472-token sequences with 56 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3064\nComponents:\n - input (input) params={\"shape\":[1,472]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,472]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22610,"embeddingDim":44}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[44]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[44]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":44,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[44]}},{"componentType":"linear","name":"head","params":{"inFeatures":44,"outFeatures":56}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":997304}} {"id":"gen-trim-3066","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2394-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3066\nComponents:\n - input (input) params={\"shape\":[1,43]}\n - fc1 (linear) params={\"inFeatures\":43,\"outFeatures\":2394}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2394,\"outFeatures\":2394}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2394,\"outFeatures\":46}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":43,"outFeatures":70}},{"type":"update_params","name":"fc2","params":{"inFeatures":70,"outFeatures":70}},{"type":"update_params","name":"head","params":{"inFeatures":70,"outFeatures":46}}],"grade":{"pass":true,"score":80,"params":11130}} {"id":"gen-norm-3067","family":"norm","seed":20260716,"spec":"This 95-feature, 18-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3067\nComponents:\n - input (input) params={\"shape\":[1,95]}\n - fc1 (linear) params={\"inFeatures\":95,\"outFeatures\":461}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":461,\"outFeatures\":170}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":170,\"outFeatures\":366}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":366,\"outFeatures\":152}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":152,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":461}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":170}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":366}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":152}}],"grade":{"pass":true,"score":100,"params":242753}} {"id":"gen-tower-3068","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 255-feature user input and a 60-feature item input, each through its own 2-layer MLP tower ending at width 426, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3068\nComponents:\n - user_input (input) params={\"shape\":[1,255]}\n - item_input (input) params={\"shape\":[1,60]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,255]}},{"componentType":"input","name":"item_input","params":{"shape":[1,60]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":255,"outFeatures":426}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":426,"outFeatures":426}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":60,"outFeatures":426}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":426,"outFeatures":426}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":852,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":497994}} {"id":"gen-grow-3069","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (54-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3069\nComponents:\n - input (input) params={\"shape\":[1,54]}\n - fc1 (linear) params={\"inFeatures\":54,\"outFeatures\":21}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":21,\"outFeatures\":21}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":21,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":54,"outFeatures":1258}},{"type":"update_params","name":"fc2","params":{"inFeatures":1258,"outFeatures":1258}},{"type":"update_params","name":"head","params":{"inFeatures":1258,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":1697042}} {"id":"gen-mlp-3070","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 116-feature input with 190 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3070\nComponents:\n - input (input) params={\"shape\":[1,116]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,116]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":116,"outFeatures":965}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":965,"outFeatures":236}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":236,"outFeatures":949}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":949,"outFeatures":190}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":743954}} {"id":"gen-ae-3071","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1529-dim input: encode down to a 212-dim bottleneck and decode back to 1529. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3071\nComponents:\n - input (input) params={\"shape\":[1,1529]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1529]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1529,"outFeatures":472}},{"componentType":"linear","name":"enc2","params":{"inFeatures":472,"outFeatures":212}},{"componentType":"linear","name":"dec1","params":{"inFeatures":212,"outFeatures":472}},{"componentType":"linear","name":"dec2","params":{"inFeatures":472,"outFeatures":1529}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1643504}} {"id":"gen-cnn-3072","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 54 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3072\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":56,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":56,"outChannels":40,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":40,"outChannels":23,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":23,"outFeatures":54}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":31194}} {"id":"gen-txf-3073","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 377-token sequences with 94 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3073\nComponents:\n - input (input) params={\"shape\":[1,377]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,377]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":35145,"embeddingDim":432}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":432,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":432,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":432,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":432,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":17462736}} {"id":"gen-gqa-3074","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 308-token sequences with 74 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3074\nComponents:\n - input (input) params={\"shape\":[1,308]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,308]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29550,"embeddingDim":256}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":256,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":256,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[256]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":256,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[256]}},{"componentType":"linear","name":"head","params":{"inFeatures":256,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":7583744}} {"id":"gen-trim-3076","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2006-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3076\nComponents:\n - input (input) params={\"shape\":[1,142]}\n - fc1 (linear) params={\"inFeatures\":142,\"outFeatures\":2006}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2006,\"outFeatures\":2006}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2006,\"outFeatures\":48}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":142,"outFeatures":91}},{"type":"update_params","name":"fc2","params":{"inFeatures":91,"outFeatures":91}},{"type":"update_params","name":"head","params":{"inFeatures":91,"outFeatures":48}}],"grade":{"pass":true,"score":80,"params":25571}} {"id":"gen-norm-3077","family":"norm","seed":20260716,"spec":"This 139-feature, 20-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3077\nComponents:\n - input (input) params={\"shape\":[1,139]}\n - fc1 (linear) params={\"inFeatures\":139,\"outFeatures\":429}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":429,\"outFeatures\":323}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":323,\"outFeatures\":20}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":429}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":323}}],"grade":{"pass":true,"score":94,"params":204658}} {"id":"gen-tower-3078","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 99-feature user input and a 157-feature item input, each through its own 2-layer MLP tower ending at width 414, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3078\nComponents:\n - user_input (input) params={\"shape\":[1,99]}\n - item_input (input) params={\"shape\":[1,157]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,99]}},{"componentType":"input","name":"item_input","params":{"shape":[1,157]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":99,"outFeatures":414}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":414,"outFeatures":414}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":157,"outFeatures":414}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":414,"outFeatures":414}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":828,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":449604}} {"id":"gen-grow-3079","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (108-feature input, 19 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3079\nComponents:\n - input (input) params={\"shape\":[1,108]}\n - fc1 (linear) params={\"inFeatures\":108,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":108,"outFeatures":1214}},{"type":"update_params","name":"fc2","params":{"inFeatures":1214,"outFeatures":1214}},{"type":"update_params","name":"head","params":{"inFeatures":1214,"outFeatures":19}}],"grade":{"pass":true,"score":80,"params":1627974}} {"id":"gen-mlp-3080","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 115-feature input with 37 output classes. Use 2 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3080\nComponents:\n - input (input) params={\"shape\":[1,115]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,115]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":115,"outFeatures":544}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":544,"outFeatures":123}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":123,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":134023}} {"id":"gen-ae-3081","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 751-dim input: encode down to a 31-dim bottleneck and decode back to 751. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3081\nComponents:\n - input (input) params={\"shape\":[1,751]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,751]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":751,"outFeatures":726}},{"componentType":"linear","name":"enc2","params":{"inFeatures":726,"outFeatures":31}},{"componentType":"linear","name":"dec1","params":{"inFeatures":31,"outFeatures":726}},{"componentType":"linear","name":"dec2","params":{"inFeatures":726,"outFeatures":751}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1135464}} {"id":"gen-cnn-3082","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x51x51 image with 25 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3082\nComponents:\n - input (input) params={\"shape\":[1,3,51,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,51,51]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":36,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":36,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":64,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":20,"outChannels":31,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":31,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39583}} {"id":"gen-txf-3083","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 202-token sequences with 16 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3083\nComponents:\n - input (input) params={\"shape\":[1,202]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,202]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":47775,"embeddingDim":40}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":40,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":40,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":40,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":40,"outFeatures":16}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1930840}} {"id":"gen-gqa-3084","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 311-token sequences with 38 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3084\nComponents:\n - input (input) params={\"shape\":[1,311]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,311]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":8169,"embeddingDim":92}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[92]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":92,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[92]}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":38}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":755044}} {"id":"gen-trim-3086","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1681-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3086\nComponents:\n - input (input) params={\"shape\":[1,225]}\n - fc1 (linear) params={\"inFeatures\":225,\"outFeatures\":1681}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1681,\"outFeatures\":1681}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1681,\"outFeatures\":1681}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1681,\"outFeatures\":1681}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1681,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":225,"outFeatures":89}},{"type":"update_params","name":"fc2","params":{"inFeatures":89,"outFeatures":89}},{"type":"update_params","name":"fc3","params":{"inFeatures":89,"outFeatures":89}},{"type":"update_params","name":"fc4","params":{"inFeatures":89,"outFeatures":89}},{"type":"update_params","name":"head","params":{"inFeatures":89,"outFeatures":47}}],"grade":{"pass":true,"score":88,"params":47971}} {"id":"gen-norm-3087","family":"norm","seed":20260716,"spec":"This 46-feature, 5-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3087\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":459}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":459,\"outFeatures\":377}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":377,\"outFeatures\":273}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":273,\"outFeatures\":184}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":184,\"outFeatures\":191}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":191,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":459}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":377}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":273}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":184}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":191}}],"grade":{"pass":true,"score":100,"params":383409}} {"id":"gen-tower-3088","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 113-feature user input and a 29-feature item input, each through its own 2-layer MLP tower ending at width 432, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3088\nComponents:\n - user_input (input) params={\"shape\":[1,113]}\n - item_input (input) params={\"shape\":[1,29]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,113]}},{"componentType":"input","name":"item_input","params":{"shape":[1,29]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":113,"outFeatures":432}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":432,"outFeatures":432}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":29,"outFeatures":432}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":432,"outFeatures":432}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":864,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":435456}} {"id":"gen-grow-3089","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (111-feature input, 37 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3089\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - fc1 (linear) params={\"inFeatures\":111,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":37}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":111,"outFeatures":1052}},{"type":"update_params","name":"fc2","params":{"inFeatures":1052,"outFeatures":1052}},{"type":"update_params","name":"head","params":{"inFeatures":1052,"outFeatures":37}}],"grade":{"pass":true,"score":80,"params":1262400}} {"id":"gen-mlp-3090","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 94-feature input with 35 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3090\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,94]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":94,"outFeatures":303}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":303,"outFeatures":562}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":562,"outFeatures":454}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":454,"outFeatures":124}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":124,"outFeatures":217}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":217,"outFeatures":581}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":581,"outFeatures":35}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":683532}} {"id":"gen-ae-3091","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 2014-dim input: encode down to a 174-dim bottleneck and decode back to 2014. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3091\nComponents:\n - input (input) params={\"shape\":[1,2014]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,2014]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":2014,"outFeatures":699}},{"componentType":"linear","name":"enc2","params":{"inFeatures":699,"outFeatures":174}},{"componentType":"linear","name":"dec1","params":{"inFeatures":174,"outFeatures":699}},{"componentType":"linear","name":"dec2","params":{"inFeatures":699,"outFeatures":2014}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3058824}} {"id":"gen-cnn-3092","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 67 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3092\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":46,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":46,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":67}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":5571}} {"id":"gen-txf-3093","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 366-token sequences with 82 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3093\nComponents:\n - input (input) params={\"shape\":[1,366]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,366]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":21856,"embeddingDim":152}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":152,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":152,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":152,"outFeatures":82}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3519408}} {"id":"gen-gqa-3094","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 227-token sequences with 57 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3094\nComponents:\n - input (input) params={\"shape\":[1,227]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,227]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":26367,"embeddingDim":360}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":360,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[360]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":360,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[360]}},{"componentType":"linear","name":"head","params":{"inFeatures":360,"outFeatures":57}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":9512640}} {"id":"gen-trim-3096","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 2657-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3096\nComponents:\n - input (input) params={\"shape\":[1,46]}\n - fc1 (linear) params={\"inFeatures\":46,\"outFeatures\":2657}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2657,\"outFeatures\":2657}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2657,\"outFeatures\":2657}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":2657,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":46,"outFeatures":239}},{"type":"update_params","name":"fc2","params":{"inFeatures":239,"outFeatures":239}},{"type":"update_params","name":"fc3","params":{"inFeatures":239,"outFeatures":239}},{"type":"update_params","name":"head","params":{"inFeatures":239,"outFeatures":30}}],"grade":{"pass":true,"score":84,"params":132406}} {"id":"gen-norm-3097","family":"norm","seed":20260716,"spec":"This 159-feature, 32-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3097\nComponents:\n - input (input) params={\"shape\":[1,159]}\n - fc1 (linear) params={\"inFeatures\":159,\"outFeatures\":143}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":143,\"outFeatures\":474}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":474,\"outFeatures\":511}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":511,\"outFeatures\":32}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":143}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":474}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":511}}],"grade":{"pass":true,"score":100,"params":349085}} {"id":"gen-tower-3098","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 201-feature user input and a 254-feature item input, each through its own 2-layer MLP tower ending at width 443, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3098\nComponents:\n - user_input (input) params={\"shape\":[1,201]}\n - item_input (input) params={\"shape\":[1,254]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,201]}},{"componentType":"input","name":"item_input","params":{"shape":[1,254]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":201,"outFeatures":443}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":443,"outFeatures":443}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":254,"outFeatures":443}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":443,"outFeatures":443}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":886,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":594949}} {"id":"gen-grow-3099","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (110-feature input, 36 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3099\nComponents:\n - input (input) params={\"shape\":[1,110]}\n - fc1 (linear) params={\"inFeatures\":110,\"outFeatures\":6}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":6,\"outFeatures\":6}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":6,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":110,"outFeatures":643}},{"type":"update_params","name":"fc2","params":{"inFeatures":643,"outFeatures":643}},{"type":"update_params","name":"head","params":{"inFeatures":643,"outFeatures":36}}],"grade":{"pass":true,"score":80,"params":507327}} {"id":"gen-mlp-3100","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 419-feature input with 160 output classes. Use 4 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3100\nComponents:\n - input (input) params={\"shape\":[1,419]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,419]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":419,"outFeatures":367}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":367,"outFeatures":584}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":584,"outFeatures":230}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":230,"outFeatures":278}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":278,"outFeatures":160}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":610841}} {"id":"gen-ae-3101","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 338-dim input: encode down to a 190-dim bottleneck and decode back to 338. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3101\nComponents:\n - input (input) params={\"shape\":[1,338]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,338]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":338,"outFeatures":105}},{"componentType":"linear","name":"enc2","params":{"inFeatures":105,"outFeatures":190}},{"componentType":"linear","name":"dec1","params":{"inFeatures":190,"outFeatures":105}},{"componentType":"linear","name":"dec2","params":{"inFeatures":105,"outFeatures":338}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":110880}} {"id":"gen-cnn-3102","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x29x29 image with 6 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3102\nComponents:\n - input (input) params={\"shape\":[1,3,29,29]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,29,29]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":13,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":13,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":20,"outChannels":38,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":38,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":61,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":30759}} {"id":"gen-txf-3103","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 197-token sequences with 75 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3103\nComponents:\n - input (input) params={\"shape\":[1,197]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,197]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":30098,"embeddingDim":144}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":144,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":144,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":144,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":4593744}} {"id":"gen-gqa-3104","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 57-token sequences with 68 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3104\nComponents:\n - input (input) params={\"shape\":[1,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,57]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":49247,"embeddingDim":560}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":560,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[560]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":560,"numHeads":16,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[560]}},{"componentType":"linear","name":"head","params":{"inFeatures":560,"outFeatures":68}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":27616400}} {"id":"gen-fix-3105","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (468) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3105\nComponents:\n - input (input) params={\"shape\":[1,307]}\n - embed (embedding) params={\"numEmbeddings\":37364,\"embeddingDim\":468}\n - attn (multiHeadAttention) params={\"embedDim\":468,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":468,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":18370872}} {"id":"gen-trim-3106","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3265-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3106\nComponents:\n - input (input) params={\"shape\":[1,178]}\n - fc1 (linear) params={\"inFeatures\":178,\"outFeatures\":3265}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3265,\"outFeatures\":3265}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3265,\"outFeatures\":3265}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3265,\"outFeatures\":3265}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3265,\"outFeatures\":42}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":178,"outFeatures":148}},{"type":"update_params","name":"fc2","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"fc3","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"fc4","params":{"inFeatures":148,"outFeatures":148}},{"type":"update_params","name":"head","params":{"inFeatures":148,"outFeatures":42}}],"grade":{"pass":true,"score":88,"params":98272}} {"id":"gen-norm-3107","family":"norm","seed":20260716,"spec":"This 237-feature, 69-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3107\nComponents:\n - input (input) params={\"shape\":[1,237]}\n - fc1 (linear) params={\"inFeatures\":237,\"outFeatures\":321}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":321,\"outFeatures\":341}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":341,\"outFeatures\":114}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":114,\"outFeatures\":303}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":303,\"outFeatures\":69}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":321}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":341}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":114}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":303}}],"grade":{"pass":true,"score":100,"params":279861}} {"id":"gen-tower-3108","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 204-feature user input and a 239-feature item input, each through its own 2-layer MLP tower ending at width 461, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3108\nComponents:\n - user_input (input) params={\"shape\":[1,204]}\n - item_input (input) params={\"shape\":[1,239]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,204]}},{"componentType":"input","name":"item_input","params":{"shape":[1,239]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":204,"outFeatures":461}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":461,"outFeatures":461}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":239,"outFeatures":461}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":461,"outFeatures":461}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":922,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":630187}} {"id":"gen-grow-3109","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (103-feature input, 45 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3109\nComponents:\n - input (input) params={\"shape\":[1,103]}\n - fc1 (linear) params={\"inFeatures\":103,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":45}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":103,"outFeatures":711}},{"type":"update_params","name":"fc2","params":{"inFeatures":711,"outFeatures":711}},{"type":"update_params","name":"head","params":{"inFeatures":711,"outFeatures":45}}],"grade":{"pass":true,"score":80,"params":610749}} {"id":"gen-mlp-3110","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 426-feature input with 105 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3110\nComponents:\n - input (input) params={\"shape\":[1,426]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,426]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":426,"outFeatures":874}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":874,"outFeatures":582}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":582,"outFeatures":827}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":827,"outFeatures":289}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":289,"outFeatures":514}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":514,"outFeatures":105}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1803825}} {"id":"gen-ae-3111","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 464-dim input: encode down to a 110-dim bottleneck and decode back to 464. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3111\nComponents:\n - input (input) params={\"shape\":[1,464]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,464]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":464,"outFeatures":715}},{"componentType":"linear","name":"enc2","params":{"inFeatures":715,"outFeatures":110}},{"componentType":"linear","name":"dec1","params":{"inFeatures":110,"outFeatures":715}},{"componentType":"linear","name":"dec2","params":{"inFeatures":715,"outFeatures":464}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":820820}} {"id":"gen-cnn-3112","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x58x58 image with 49 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3112\nComponents:\n - input (input) params={\"shape\":[1,3,58,58]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,58,58]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":60,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":51,"outChannels":12,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":12,"outFeatures":49}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":35256}} {"id":"gen-txf-3113","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 460-token sequences with 85 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3113\nComponents:\n - input (input) params={\"shape\":[1,460]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,460]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":28681,"embeddingDim":236}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":236,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":236,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":236,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":7234344}} {"id":"gen-gqa-3114","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 474-token sequences with 25 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3114\nComponents:\n - input (input) params={\"shape\":[1,474]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,474]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":48997,"embeddingDim":104}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":104,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[104]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":104,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[104]}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":25}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":5098288}} {"id":"gen-trim-3116","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 3672-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3116\nComponents:\n - input (input) params={\"shape\":[1,157]}\n - fc1 (linear) params={\"inFeatures\":157,\"outFeatures\":3672}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3672,\"outFeatures\":3672}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":3672,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":157,"outFeatures":110}},{"type":"update_params","name":"fc2","params":{"inFeatures":110,"outFeatures":110}},{"type":"update_params","name":"head","params":{"inFeatures":110,"outFeatures":24}}],"grade":{"pass":true,"score":80,"params":32010}} {"id":"gen-norm-3117","family":"norm","seed":20260716,"spec":"This 75-feature, 47-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3117\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":410}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":410,\"outFeatures\":51}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":51,\"outFeatures\":336}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":336,\"outFeatures\":305}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":305,\"outFeatures\":169}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":169,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":410}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":51}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":336}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":305}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":169}}],"grade":{"pass":true,"score":100,"params":230764}} {"id":"gen-tower-3118","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 59-feature user input and a 81-feature item input, each through its own 2-layer MLP tower ending at width 219, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3118\nComponents:\n - user_input (input) params={\"shape\":[1,59]}\n - item_input (input) params={\"shape\":[1,81]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,59]}},{"componentType":"input","name":"item_input","params":{"shape":[1,81]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":59,"outFeatures":219}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":219,"outFeatures":219}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":81,"outFeatures":219}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":219,"outFeatures":219}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":438,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":127020}} {"id":"gen-grow-3119","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (32-feature input, 16 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3119\nComponents:\n - input (input) params={\"shape\":[1,32]}\n - fc1 (linear) params={\"inFeatures\":32,\"outFeatures\":20}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":20,\"outFeatures\":20}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":20,\"outFeatures\":16}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":32,"outFeatures":839}},{"type":"update_params","name":"fc2","params":{"inFeatures":839,"outFeatures":839}},{"type":"update_params","name":"head","params":{"inFeatures":839,"outFeatures":16}}],"grade":{"pass":true,"score":80,"params":744193}} {"id":"gen-mlp-3120","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 19-feature input with 9 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3120\nComponents:\n - input (input) params={\"shape\":[1,19]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,19]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":19,"outFeatures":847}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":847,"outFeatures":93}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":93,"outFeatures":486}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":486,"outFeatures":297}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":297,"outFeatures":363}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":363,"outFeatures":9}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":395482}} {"id":"gen-ae-3121","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1195-dim input: encode down to a 169-dim bottleneck and decode back to 1195. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3121\nComponents:\n - input (input) params={\"shape\":[1,1195]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1195]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1195,"outFeatures":230}},{"componentType":"linear","name":"enc2","params":{"inFeatures":230,"outFeatures":169}},{"componentType":"linear","name":"dec1","params":{"inFeatures":169,"outFeatures":230}},{"componentType":"linear","name":"dec2","params":{"inFeatures":230,"outFeatures":1195}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":627440}} {"id":"gen-cnn-3122","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x24x24 image with 70 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3122\nComponents:\n - input (input) params={\"shape\":[1,3,24,24]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,24,24]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":14,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":14,"outChannels":51,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":51,"outChannels":60,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":60,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":39834}} {"id":"gen-txf-3123","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 372-token sequences with 44 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3123\nComponents:\n - input (input) params={\"shape\":[1,372]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,372]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13764,"embeddingDim":70}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":70,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":70,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":986160}} {"id":"gen-gqa-3124","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 34-token sequences with 18 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3124\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,34]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":9200,"embeddingDim":344}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":344,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[344]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":344,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[344]}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":18}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3170992}} {"id":"gen-trim-3126","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3221-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3126\nComponents:\n - input (input) params={\"shape\":[1,166]}\n - fc1 (linear) params={\"inFeatures\":166,\"outFeatures\":3221}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3221,\"outFeatures\":3221}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3221,\"outFeatures\":3221}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3221,\"outFeatures\":36}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":166,"outFeatures":370}},{"type":"update_params","name":"fc2","params":{"inFeatures":370,"outFeatures":370}},{"type":"update_params","name":"fc3","params":{"inFeatures":370,"outFeatures":370}},{"type":"update_params","name":"head","params":{"inFeatures":370,"outFeatures":36}}],"grade":{"pass":true,"score":84,"params":348540}} {"id":"gen-norm-3127","family":"norm","seed":20260716,"spec":"This 148-feature, 89-class MLP trains unstably. Insert a batchNorm1d after each of the 2 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3127\nComponents:\n - input (input) params={\"shape\":[1,148]}\n - fc1 (linear) params={\"inFeatures\":148,\"outFeatures\":144}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":144,\"outFeatures\":185}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":185,\"outFeatures\":89}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":9,"mustReachOutput":true,"maxActions":3,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":144}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":185}}],"grade":{"pass":true,"score":94,"params":64417}} {"id":"gen-tower-3128","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 207-feature user input and a 136-feature item input, each through its own 2-layer MLP tower ending at width 62, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3128\nComponents:\n - user_input (input) params={\"shape\":[1,207]}\n - item_input (input) params={\"shape\":[1,136]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,207]}},{"componentType":"input","name":"item_input","params":{"shape":[1,136]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":207,"outFeatures":62}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":136,"outFeatures":62}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":62,"outFeatures":62}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":124,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":29078}} {"id":"gen-grow-3129","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (59-feature input, 49 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3129\nComponents:\n - input (input) params={\"shape\":[1,59]}\n - fc1 (linear) params={\"inFeatures\":59,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":49}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":59,"outFeatures":939}},{"type":"update_params","name":"fc2","params":{"inFeatures":939,"outFeatures":939}},{"type":"update_params","name":"head","params":{"inFeatures":939,"outFeatures":49}}],"grade":{"pass":true,"score":80,"params":983133}} {"id":"gen-mlp-3130","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 51-feature input with 72 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3130\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,51]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":51,"outFeatures":941}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":941,"outFeatures":756}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":756,"outFeatures":759}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":759,"outFeatures":368}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":368,"outFeatures":48}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1633623}} {"id":"gen-ae-3131","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 512-dim input: encode down to a 118-dim bottleneck and decode back to 512. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3131\nComponents:\n - input (input) params={\"shape\":[1,512]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,512]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":512,"outFeatures":114}},{"componentType":"linear","name":"enc2","params":{"inFeatures":114,"outFeatures":118}},{"componentType":"linear","name":"dec1","params":{"inFeatures":118,"outFeatures":114}},{"componentType":"linear","name":"dec2","params":{"inFeatures":114,"outFeatures":512}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":143640}} {"id":"gen-cnn-3132","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x30x30 image with 11 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3132\nComponents:\n - input (input) params={\"shape\":[1,3,30,30]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,30,30]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":62,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":62,"outChannels":17,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":17,"outFeatures":11}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":11347}} {"id":"gen-txf-3133","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 342-token sequences with 93 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3133\nComponents:\n - input (input) params={\"shape\":[1,342]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,342]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":24448,"embeddingDim":92}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":92,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":92,"outFeatures":93}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":2291628}} {"id":"gen-gqa-3134","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 34-token sequences with 74 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3134\nComponents:\n - input (input) params={\"shape\":[1,34]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,34]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":20083,"embeddingDim":96}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[96]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":96,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[96]}},{"componentType":"linear","name":"head","params":{"inFeatures":96,"outFeatures":74}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1935072}} {"id":"gen-trim-3136","family":"trim","seed":20260716,"spec":"This 3-hidden-layer MLP uses 3163-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 3 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3136\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - fc1 (linear) params={\"inFeatures\":201,\"outFeatures\":3163}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3163,\"outFeatures\":3163}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3163,\"outFeatures\":3163}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":3163,\"outFeatures\":24}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":201,"outFeatures":255}},{"type":"update_params","name":"fc2","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"fc3","params":{"inFeatures":255,"outFeatures":255}},{"type":"update_params","name":"head","params":{"inFeatures":255,"outFeatures":24}}],"grade":{"pass":true,"score":84,"params":187425}} {"id":"gen-norm-3137","family":"norm","seed":20260716,"spec":"This 78-feature, 63-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3137\nComponents:\n - input (input) params={\"shape\":[1,78]}\n - fc1 (linear) params={\"inFeatures\":78,\"outFeatures\":237}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":237,\"outFeatures\":318}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":318,\"outFeatures\":475}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":475,\"outFeatures\":63}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":237}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":318}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":475}}],"grade":{"pass":true,"score":100,"params":274827}} {"id":"gen-tower-3138","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 102-feature user input and a 134-feature item input, each through its own 2-layer MLP tower ending at width 213, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3138\nComponents:\n - user_input (input) params={\"shape\":[1,102]}\n - item_input (input) params={\"shape\":[1,134]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,102]}},{"componentType":"input","name":"item_input","params":{"shape":[1,134]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":102,"outFeatures":213}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":213,"outFeatures":213}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":134,"outFeatures":213}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":213,"outFeatures":213}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":426,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":141432}} {"id":"gen-grow-3139","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (42-feature input, 41 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3139\nComponents:\n - input (input) params={\"shape\":[1,42]}\n - fc1 (linear) params={\"inFeatures\":42,\"outFeatures\":9}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":9,\"outFeatures\":9}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":9,\"outFeatures\":41}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":42,"outFeatures":1163}},{"type":"update_params","name":"fc2","params":{"inFeatures":1163,"outFeatures":1163}},{"type":"update_params","name":"head","params":{"inFeatures":1163,"outFeatures":41}}],"grade":{"pass":true,"score":80,"params":1449098}} {"id":"gen-mlp-3140","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 201-feature input with 126 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3140\nComponents:\n - input (input) params={\"shape\":[1,201]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,201]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":201,"outFeatures":399}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":399,"outFeatures":307}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":307,"outFeatures":965}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":965,"outFeatures":126}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":620537}} {"id":"gen-ae-3141","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 999-dim input: encode down to a 116-dim bottleneck and decode back to 999. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3141\nComponents:\n - input (input) params={\"shape\":[1,999]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,999]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":999,"outFeatures":700}},{"componentType":"linear","name":"enc2","params":{"inFeatures":700,"outFeatures":116}},{"componentType":"linear","name":"dec1","params":{"inFeatures":116,"outFeatures":700}},{"componentType":"linear","name":"dec2","params":{"inFeatures":700,"outFeatures":999}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":1561000}} {"id":"gen-cnn-3142","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x47x47 image with 48 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3142\nComponents:\n - input (input) params={\"shape\":[1,3,47,47]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,47,47]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":58,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":58,"outChannels":49,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":49,"outChannels":9,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":9,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":31545}} {"id":"gen-txf-3143","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 312-token sequences with 62 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3143\nComponents:\n - input (input) params={\"shape\":[1,312]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,312]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":46513,"embeddingDim":156}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":156,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":156,"outFeatures":62}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":7363044}} {"id":"gen-gqa-3144","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 230-token sequences with 72 output classes: token embedding, 1 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3144\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,230]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22722,"embeddingDim":48}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":48,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[48]}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":72}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":73,"params":1094112}} {"id":"gen-fix-3145","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (220) is not divisible by numHeads (7). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3145\nComponents:\n - input (input) params={\"shape\":[1,89]}\n - embed (embedding) params={\"numEmbeddings\":9183,\"embeddingDim\":220}\n - attn (multiHeadAttention) params={\"embedDim\":220,\"numHeads\":7}\n - head (linear) params={\"inFeatures\":220,\"outFeatures\":80}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":2231460}} {"id":"gen-trim-3146","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 1670-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3146\nComponents:\n - input (input) params={\"shape\":[1,97]}\n - fc1 (linear) params={\"inFeatures\":97,\"outFeatures\":1670}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":1670,\"outFeatures\":1670}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":1670,\"outFeatures\":1670}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":1670,\"outFeatures\":1670}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":1670,\"outFeatures\":19}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":97,"outFeatures":263}},{"type":"update_params","name":"fc2","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"fc3","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"fc4","params":{"inFeatures":263,"outFeatures":263}},{"type":"update_params","name":"head","params":{"inFeatures":263,"outFeatures":19}}],"grade":{"pass":true,"score":88,"params":238015}} {"id":"gen-norm-3147","family":"norm","seed":20260716,"spec":"This 39-feature, 5-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3147\nComponents:\n - input (input) params={\"shape\":[1,39]}\n - fc1 (linear) params={\"inFeatures\":39,\"outFeatures\":310}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":310,\"outFeatures\":394}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":394,\"outFeatures\":71}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":71,\"outFeatures\":457}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":457,\"outFeatures\":5}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":310}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":394}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":71}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":457}}],"grade":{"pass":true,"score":100,"params":196936}} {"id":"gen-tower-3148","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 109-feature user input and a 100-feature item input, each through its own 2-layer MLP tower ending at width 277, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3148\nComponents:\n - user_input (input) params={\"shape\":[1,109]}\n - item_input (input) params={\"shape\":[1,100]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,109]}},{"componentType":"input","name":"item_input","params":{"shape":[1,100]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":109,"outFeatures":277}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":277,"outFeatures":277}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":100,"outFeatures":277}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":277,"outFeatures":277}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":554,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":211905}} {"id":"gen-grow-3149","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (64-feature input, 18 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3149\nComponents:\n - input (input) params={\"shape\":[1,64]}\n - fc1 (linear) params={\"inFeatures\":64,\"outFeatures\":10}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":10,\"outFeatures\":10}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":10,\"outFeatures\":18}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":64,"outFeatures":1135}},{"type":"update_params","name":"fc2","params":{"inFeatures":1135,"outFeatures":1135}},{"type":"update_params","name":"head","params":{"inFeatures":1135,"outFeatures":18}}],"grade":{"pass":true,"score":80,"params":1381295}} {"id":"gen-mlp-3150","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 90-feature input with 186 output classes. Use 3 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3150\nComponents:\n - input (input) params={\"shape\":[1,90]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,90]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":90,"outFeatures":181}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":181,"outFeatures":655}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":655,"outFeatures":330}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":330,"outFeatures":186}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":412375}} {"id":"gen-ae-3151","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1908-dim input: encode down to a 18-dim bottleneck and decode back to 1908. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3151\nComponents:\n - input (input) params={\"shape\":[1,1908]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1908]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1908,"outFeatures":183}},{"componentType":"linear","name":"enc2","params":{"inFeatures":183,"outFeatures":18}},{"componentType":"linear","name":"dec1","params":{"inFeatures":18,"outFeatures":183}},{"componentType":"linear","name":"dec2","params":{"inFeatures":183,"outFeatures":1908}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":704916}} {"id":"gen-cnn-3152","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x52x52 image with 13 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3152\nComponents:\n - input (input) params={\"shape\":[1,3,52,52]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,52,52]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":41,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":41,"outChannels":53,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":53,"outFeatures":13}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":21353}} {"id":"gen-txf-3153","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 457-token sequences with 61 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3153\nComponents:\n - input (input) params={\"shape\":[1,457]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,457]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":43370,"embeddingDim":128}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":128,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":128,"numHeads":8}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":128,"numHeads":8}},{"componentType":"linear","name":"head","params":{"inFeatures":128,"outFeatures":61}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":5755776}} {"id":"gen-gqa-3154","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 53-token sequences with 37 output classes: token embedding, 3 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3154\nComponents:\n - input (input) params={\"shape\":[1,53]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":10,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,53]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":10713,"embeddingDim":184}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[184]}},{"componentType":"groupedQueryAttention","name":"attn3","params":{"embedDim":184,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm3","params":{"normalizedShape":[184]}},{"componentType":"linear","name":"head","params":{"inFeatures":184,"outFeatures":37}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"attn3"},{"from":"attn3","to":"norm3"},{"from":"norm3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":81,"params":1978000}} {"id":"gen-fix-3155","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (484) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3155\nComponents:\n - input (input) params={\"shape\":[1,307]}\n - embed (embedding) params={\"numEmbeddings\":33320,\"embeddingDim\":484}\n - attn (multiHeadAttention) params={\"embedDim\":484,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":484,\"outFeatures\":33}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":4}}],"grade":{"pass":true,"score":60,"params":17079876}} {"id":"gen-trim-3156","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2458-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3156\nComponents:\n - input (input) params={\"shape\":[1,187]}\n - fc1 (linear) params={\"inFeatures\":187,\"outFeatures\":2458}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2458,\"outFeatures\":2458}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2458,\"outFeatures\":2458}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2458,\"outFeatures\":2458}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2458,\"outFeatures\":4}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":187,"outFeatures":135}},{"type":"update_params","name":"fc2","params":{"inFeatures":135,"outFeatures":135}},{"type":"update_params","name":"fc3","params":{"inFeatures":135,"outFeatures":135}},{"type":"update_params","name":"fc4","params":{"inFeatures":135,"outFeatures":135}},{"type":"update_params","name":"head","params":{"inFeatures":135,"outFeatures":4}}],"grade":{"pass":true,"score":88,"params":80460}} {"id":"gen-norm-3157","family":"norm","seed":20260716,"spec":"This 94-feature, 30-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3157\nComponents:\n - input (input) params={\"shape\":[1,94]}\n - fc1 (linear) params={\"inFeatures\":94,\"outFeatures\":451}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":451,\"outFeatures\":109}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":109,\"outFeatures\":243}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":243,\"outFeatures\":496}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":496,\"outFeatures\":30}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":451}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":109}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":243}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":496}}],"grade":{"pass":true,"score":100,"params":253448}} {"id":"gen-tower-3158","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 219-feature user input and a 37-feature item input, each through its own 2-layer MLP tower ending at width 389, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3158\nComponents:\n - user_input (input) params={\"shape\":[1,219]}\n - item_input (input) params={\"shape\":[1,37]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,219]}},{"componentType":"input","name":"item_input","params":{"shape":[1,37]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":219,"outFeatures":389}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":389,"outFeatures":389}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":37,"outFeatures":389}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":389,"outFeatures":389}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":778,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":403004}} {"id":"gen-grow-3159","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (63-feature input, 43 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3159\nComponents:\n - input (input) params={\"shape\":[1,63]}\n - fc1 (linear) params={\"inFeatures\":63,\"outFeatures\":12}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":12,\"outFeatures\":12}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":12,\"outFeatures\":43}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":63,"outFeatures":1137}},{"type":"update_params","name":"fc2","params":{"inFeatures":1137,"outFeatures":1137}},{"type":"update_params","name":"head","params":{"inFeatures":1137,"outFeatures":43}}],"grade":{"pass":true,"score":80,"params":1413291}} {"id":"gen-mlp-3160","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 70-feature input with 162 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3160\nComponents:\n - input (input) params={\"shape\":[1,70]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,70]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":70,"outFeatures":571}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":571,"outFeatures":528}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":528,"outFeatures":100}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":100,"outFeatures":455}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":455,"outFeatures":539}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":539,"outFeatures":162}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":772321}} {"id":"gen-ae-3161","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 381-dim input: encode down to a 13-dim bottleneck and decode back to 381. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3161\nComponents:\n - input (input) params={\"shape\":[1,381]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,381]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":381,"outFeatures":418}},{"componentType":"linear","name":"enc2","params":{"inFeatures":418,"outFeatures":13}},{"componentType":"linear","name":"dec1","params":{"inFeatures":13,"outFeatures":418}},{"componentType":"linear","name":"dec2","params":{"inFeatures":418,"outFeatures":381}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":329384}} {"id":"gen-cnn-3162","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x35x35 image with 70 classes. Use 4 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3162\nComponents:\n - input (input) params={\"shape\":[1,3,35,35]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,35,35]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":35,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":35,"outChannels":54,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":54,"outChannels":64,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"conv2d","name":"conv4","params":{"inChannels":64,"outChannels":47,"kernelSize":3}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":47,"outFeatures":70}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"conv4"},{"from":"conv4","to":"act4"},{"from":"act4","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":79421}} {"id":"gen-txf-3163","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 371-token sequences with 63 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3163\nComponents:\n - input (input) params={\"shape\":[1,371]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,371]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":31252,"embeddingDim":100}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":100,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":100,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":100,"outFeatures":63}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3211500}} {"id":"gen-gqa-3164","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 459-token sequences with 94 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3164\nComponents:\n - input (input) params={\"shape\":[1,459]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,459]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":38735,"embeddingDim":384}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[384]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":384,"numHeads":8,"numKVHeads":4}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[384]}},{"componentType":"linear","name":"head","params":{"inFeatures":384,"outFeatures":94}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":14910336}} {"id":"gen-trim-3166","family":"trim","seed":20260716,"spec":"This 2-hidden-layer MLP uses 2320-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 2 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3166\nComponents:\n - input (input) params={\"shape\":[1,75]}\n - fc1 (linear) params={\"inFeatures\":75,\"outFeatures\":2320}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2320,\"outFeatures\":2320}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":2320,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":75,"outFeatures":119}},{"type":"update_params","name":"fc2","params":{"inFeatures":119,"outFeatures":119}},{"type":"update_params","name":"head","params":{"inFeatures":119,"outFeatures":17}}],"grade":{"pass":true,"score":80,"params":25109}} {"id":"gen-norm-3167","family":"norm","seed":20260716,"spec":"This 111-feature, 84-class MLP trains unstably. Insert a batchNorm1d after each of the 5 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3167\nComponents:\n - input (input) params={\"shape\":[1,111]}\n - fc1 (linear) params={\"inFeatures\":111,\"outFeatures\":237}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":237,\"outFeatures\":266}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":266,\"outFeatures\":116}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":116,\"outFeatures\":192}\n - act4 (relu)\n - fc5 (linear) params={\"inFeatures\":192,\"outFeatures\":333}\n - act5 (relu)\n - head (linear) params={\"inFeatures\":333,\"outFeatures\":84}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> fc5\n - fc5 -> act5\n - act5 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":18,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":237}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":266}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":116}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":192}},{"type":"add_component","componentType":"batchNorm1d","name":"bn5","afterName":"fc5","params":{"numFeatures":333}}],"grade":{"pass":true,"score":100,"params":234385}} {"id":"gen-tower-3168","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 216-feature user input and a 240-feature item input, each through its own 2-layer MLP tower ending at width 197, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3168\nComponents:\n - user_input (input) params={\"shape\":[1,216]}\n - item_input (input) params={\"shape\":[1,240]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,216]}},{"componentType":"input","name":"item_input","params":{"shape":[1,240]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":216,"outFeatures":197}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":197,"outFeatures":197}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":240,"outFeatures":197}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":197,"outFeatures":197}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":394,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":167844}} {"id":"gen-grow-3169","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (20-feature input, 11 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3169\nComponents:\n - input (input) params={\"shape\":[1,20]}\n - fc1 (linear) params={\"inFeatures\":20,\"outFeatures\":4}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":4,\"outFeatures\":4}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":4,\"outFeatures\":11}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":20,"outFeatures":963}},{"type":"update_params","name":"fc2","params":{"inFeatures":963,"outFeatures":963}},{"type":"update_params","name":"head","params":{"inFeatures":963,"outFeatures":11}}],"grade":{"pass":true,"score":80,"params":957222}} {"id":"gen-mlp-3170","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 401-feature input with 96 output classes. Use 6 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3170\nComponents:\n - input (input) params={\"shape\":[1,401]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":15,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,401]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":401,"outFeatures":810}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":810,"outFeatures":189}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":189,"outFeatures":314}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":314,"outFeatures":233}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":233,"outFeatures":462}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"fc6","params":{"inFeatures":462,"outFeatures":825}},{"componentType":"relu","name":"act6","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":825,"outFeatures":96}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"fc6"},{"from":"fc6","to":"act6"},{"from":"act6","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1178404}} {"id":"gen-ae-3171","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 372-dim input: encode down to a 230-dim bottleneck and decode back to 372. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3171\nComponents:\n - input (input) params={\"shape\":[1,372]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,372]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":372,"outFeatures":102}},{"componentType":"linear","name":"enc2","params":{"inFeatures":102,"outFeatures":230}},{"componentType":"linear","name":"dec1","params":{"inFeatures":230,"outFeatures":102}},{"componentType":"linear","name":"dec2","params":{"inFeatures":102,"outFeatures":372}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":122808}} {"id":"gen-cnn-3172","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x57x57 image with 48 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3172\nComponents:\n - input (input) params={\"shape\":[1,3,57,57]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,57,57]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":33,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":33,"outChannels":42,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":42,"outFeatures":48}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":15381}} {"id":"gen-txf-3173","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 183-token sequences with 87 output classes. Token embedding, 1 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3173\nComponents:\n - input (input) params={\"shape\":[1,183]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":5,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,183]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":37760,"embeddingDim":144}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":144,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":144,"outFeatures":87}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":60,"params":5532912}} {"id":"gen-gqa-3174","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 371-token sequences with 75 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3174\nComponents:\n - input (input) params={\"shape\":[1,371]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,371]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":17484,"embeddingDim":192}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":192,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[192]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":192,"numHeads":8,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[192]}},{"componentType":"linear","name":"head","params":{"inFeatures":192,"outFeatures":75}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":3371328}} {"id":"gen-fix-3175","family":"fix","seed":20260716,"spec":"This encoder fails validation: embedDim (458) is not divisible by numHeads (5). Repair the attention configuration in place with at most 2 actions. Do not rebuild the model from scratch.","observation":"Model: gen-fix-3175\nComponents:\n - input (input) params={\"shape\":[1,100]}\n - embed (embedding) params={\"numEmbeddings\":49636,\"embeddingDim\":458}\n - attn (multiHeadAttention) params={\"embedDim\":458,\"numHeads\":5}\n - head (linear) params={\"inFeatures\":458,\"outFeatures\":47}\n - output (output)\nConnections:\n - input -> embed\n - embed -> attn\n - attn -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustReachOutput":true,"minComponents":5,"maxActions":2,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"attn","params":{"numHeads":2}}],"grade":{"pass":true,"score":60,"params":23593870}} {"id":"gen-trim-3176","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 3076-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3176\nComponents:\n - input (input) params={\"shape\":[1,210]}\n - fc1 (linear) params={\"inFeatures\":210,\"outFeatures\":3076}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":3076,\"outFeatures\":3076}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":3076,\"outFeatures\":3076}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":3076,\"outFeatures\":3076}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":3076,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":210,"outFeatures":241}},{"type":"update_params","name":"fc2","params":{"inFeatures":241,"outFeatures":241}},{"type":"update_params","name":"fc3","params":{"inFeatures":241,"outFeatures":241}},{"type":"update_params","name":"fc4","params":{"inFeatures":241,"outFeatures":241}},{"type":"update_params","name":"head","params":{"inFeatures":241,"outFeatures":39}}],"grade":{"pass":true,"score":88,"params":234252}} {"id":"gen-norm-3177","family":"norm","seed":20260716,"spec":"This 106-feature, 40-class MLP trains unstably. Insert a batchNorm1d after each of the 3 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3177\nComponents:\n - input (input) params={\"shape\":[1,106]}\n - fc1 (linear) params={\"inFeatures\":106,\"outFeatures\":506}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":506,\"outFeatures\":301}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":301,\"outFeatures\":176}\n - act3 (relu)\n - head (linear) params={\"inFeatures\":176,\"outFeatures\":40}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":12,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":506}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":301}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":176}}],"grade":{"pass":true,"score":100,"params":265958}} {"id":"gen-tower-3178","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 41-feature user input and a 234-feature item input, each through its own 2-layer MLP tower ending at width 242, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3178\nComponents:\n - user_input (input) params={\"shape\":[1,41]}\n - item_input (input) params={\"shape\":[1,234]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,41]}},{"componentType":"input","name":"item_input","params":{"shape":[1,234]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":41,"outFeatures":242}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":242,"outFeatures":242}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":234,"outFeatures":242}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":242,"outFeatures":242}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":484,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":184162}} {"id":"gen-grow-3179","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (33-feature input, 15 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3179\nComponents:\n - input (input) params={\"shape\":[1,33]}\n - fc1 (linear) params={\"inFeatures\":33,\"outFeatures\":11}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":11,\"outFeatures\":11}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":11,\"outFeatures\":15}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":33,"outFeatures":794}},{"type":"update_params","name":"fc2","params":{"inFeatures":794,"outFeatures":794}},{"type":"update_params","name":"head","params":{"inFeatures":794,"outFeatures":15}}],"grade":{"pass":true,"score":80,"params":668548}} {"id":"gen-mlp-3180","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 121-feature input with 135 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3180\nComponents:\n - input (input) params={\"shape\":[1,121]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,121]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":121,"outFeatures":443}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":443,"outFeatures":767}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":767,"outFeatures":847}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":847,"outFeatures":713}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":713,"outFeatures":344}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":344,"outFeatures":135}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1938656}} {"id":"gen-ae-3181","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1679-dim input: encode down to a 219-dim bottleneck and decode back to 1679. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3181\nComponents:\n - input (input) params={\"shape\":[1,1679]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1679]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1679,"outFeatures":572}},{"componentType":"linear","name":"enc2","params":{"inFeatures":572,"outFeatures":219}},{"componentType":"linear","name":"dec1","params":{"inFeatures":219,"outFeatures":572}},{"componentType":"linear","name":"dec2","params":{"inFeatures":572,"outFeatures":1679}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2171312}} {"id":"gen-cnn-3182","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x56x56 image with 6 classes. Use 2 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3182\nComponents:\n - input (input) params={\"shape\":[1,3,56,56]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,56,56]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":61,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":61,"outChannels":48,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":48,"outFeatures":6}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":80,"params":28287}} {"id":"gen-txf-3183","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 217-token sequences with 44 output classes. Token embedding, 3 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3183\nComponents:\n - input (input) params={\"shape\":[1,217]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":7,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,217]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":13727,"embeddingDim":104}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":104,"numHeads":4}},{"componentType":"multiHeadAttention","name":"attn3","params":{"embedDim":104,"numHeads":4}},{"componentType":"linear","name":"head","params":{"inFeatures":104,"outFeatures":44}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"attn3"},{"from":"attn3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":65,"params":1561976}} {"id":"gen-gqa-3184","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 501-token sequences with 52 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3184\nComponents:\n - input (input) params={\"shape\":[1,501]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,501]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":34050,"embeddingDim":64}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[64]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":64,"numHeads":4,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[64]}},{"componentType":"linear","name":"head","params":{"inFeatures":64,"outFeatures":52}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":2182528}} {"id":"gen-trim-3186","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2063-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3186\nComponents:\n - input (input) params={\"shape\":[1,83]}\n - fc1 (linear) params={\"inFeatures\":83,\"outFeatures\":2063}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2063,\"outFeatures\":2063}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2063,\"outFeatures\":2063}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2063,\"outFeatures\":2063}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2063,\"outFeatures\":17}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":83,"outFeatures":72}},{"type":"update_params","name":"fc2","params":{"inFeatures":72,"outFeatures":72}},{"type":"update_params","name":"fc3","params":{"inFeatures":72,"outFeatures":72}},{"type":"update_params","name":"fc4","params":{"inFeatures":72,"outFeatures":72}},{"type":"update_params","name":"head","params":{"inFeatures":72,"outFeatures":17}}],"grade":{"pass":true,"score":88,"params":22752}} {"id":"gen-norm-3187","family":"norm","seed":20260716,"spec":"This 127-feature, 87-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3187\nComponents:\n - input (input) params={\"shape\":[1,127]}\n - fc1 (linear) params={\"inFeatures\":127,\"outFeatures\":52}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":52,\"outFeatures\":226}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":226,\"outFeatures\":495}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":495,\"outFeatures\":498}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":498,\"outFeatures\":87}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":52}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":226}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":495}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":498}}],"grade":{"pass":true,"score":100,"params":420062}} {"id":"gen-tower-3188","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 230-feature user input and a 41-feature item input, each through its own 2-layer MLP tower ending at width 131, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3188\nComponents:\n - user_input (input) params={\"shape\":[1,230]}\n - item_input (input) params={\"shape\":[1,41]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,230]}},{"componentType":"input","name":"item_input","params":{"shape":[1,41]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":230,"outFeatures":131}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":131,"outFeatures":131}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":41,"outFeatures":131}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":131,"outFeatures":131}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":262,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":70085}} {"id":"gen-grow-3189","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (51-feature input, 39 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3189\nComponents:\n - input (input) params={\"shape\":[1,51]}\n - fc1 (linear) params={\"inFeatures\":51,\"outFeatures\":16}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":16,\"outFeatures\":16}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":16,\"outFeatures\":39}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":51,"outFeatures":661}},{"type":"update_params","name":"fc2","params":{"inFeatures":661,"outFeatures":661}},{"type":"update_params","name":"head","params":{"inFeatures":661,"outFeatures":39}}],"grade":{"pass":true,"score":80,"params":496411}} {"id":"gen-mlp-3190","family":"mlp","seed":20260716,"spec":"Design a dense classifier for a 458-feature input with 191 output classes. Use 5 hidden linear layers with ReLU and a linear head. Keep it under 10M params.","observation":"Model: gen-mlp-3190\nComponents:\n - input (input) params={\"shape\":[1,458]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu"],"minComponents":13,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,458]}},{"componentType":"linear","name":"fc1","params":{"inFeatures":458,"outFeatures":828}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"linear","name":"fc2","params":{"inFeatures":828,"outFeatures":444}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"linear","name":"fc3","params":{"inFeatures":444,"outFeatures":396}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"fc4","params":{"inFeatures":396,"outFeatures":31}},{"componentType":"relu","name":"act4","params":{}},{"componentType":"linear","name":"fc5","params":{"inFeatures":31,"outFeatures":1018}},{"componentType":"relu","name":"act5","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":1018,"outFeatures":191}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"fc1"},{"from":"fc1","to":"act1"},{"from":"act1","to":"fc2"},{"from":"fc2","to":"act2"},{"from":"act2","to":"fc3"},{"from":"fc3","to":"act3"},{"from":"act3","to":"fc4"},{"from":"fc4","to":"act4"},{"from":"act4","to":"fc5"},{"from":"fc5","to":"act5"},{"from":"act5","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":90,"params":1160952}} {"id":"gen-ae-3191","family":"ae","seed":20260716,"spec":"Design a dense autoencoder for 1914-dim input: encode down to a 151-dim bottleneck and decode back to 1914. Keep it valid and connected, under 20M params.","observation":"Model: gen-ae-3191\nComponents:\n - input (input) params={\"shape\":[1,1914]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":20000000,"mustContainTypes":["linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,1914]}},{"componentType":"linear","name":"enc1","params":{"inFeatures":1914,"outFeatures":805}},{"componentType":"linear","name":"enc2","params":{"inFeatures":805,"outFeatures":151}},{"componentType":"linear","name":"dec1","params":{"inFeatures":151,"outFeatures":805}},{"componentType":"linear","name":"dec2","params":{"inFeatures":805,"outFeatures":1914}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"enc1"},{"from":"enc1","to":"enc2"},{"from":"enc2","to":"dec1"},{"from":"dec1","to":"dec2"},{"from":"dec2","to":"output"}]}],"grade":{"pass":true,"score":63,"params":3324650}} {"id":"gen-cnn-3192","family":"cnn","seed":20260716,"spec":"Design a convolutional classifier for a 3x49x49 image with 85 classes. Use 3 conv layers with ReLU and a linear head. Keep it under 50M params.","observation":"Model: gen-cnn-3192\nComponents:\n - input (input) params={\"shape\":[1,3,49,49]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":50000000,"mustContainTypes":["conv2d","linear"],"minComponents":9,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,3,49,49]}},{"componentType":"conv2d","name":"conv1","params":{"inChannels":3,"outChannels":10,"kernelSize":3}},{"componentType":"relu","name":"act1","params":{}},{"componentType":"conv2d","name":"conv2","params":{"inChannels":10,"outChannels":20,"kernelSize":3}},{"componentType":"relu","name":"act2","params":{}},{"componentType":"conv2d","name":"conv3","params":{"inChannels":20,"outChannels":28,"kernelSize":3}},{"componentType":"relu","name":"act3","params":{}},{"componentType":"linear","name":"head","params":{"inFeatures":28,"outFeatures":85}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"conv1"},{"from":"conv1","to":"act1"},{"from":"act1","to":"conv2"},{"from":"conv2","to":"act2"},{"from":"act2","to":"conv3"},{"from":"conv3","to":"act3"},{"from":"act3","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":84,"params":9490}} {"id":"gen-txf-3193","family":"txf","seed":20260716,"spec":"Design a transformer encoder for 163-token sequences with 32 output classes. Token embedding, 2 multi-head attention block(s) with a valid head configuration, then a linear head. Keep it under 60M params.","observation":"Model: gen-txf-3193\nComponents:\n - input (input) params={\"shape\":[1,163]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","multiHeadAttention","linear"],"minComponents":6,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,163]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":22528,"embeddingDim":110}},{"componentType":"multiHeadAttention","name":"attn1","params":{"embedDim":110,"numHeads":2}},{"componentType":"multiHeadAttention","name":"attn2","params":{"embedDim":110,"numHeads":2}},{"componentType":"linear","name":"head","params":{"inFeatures":110,"outFeatures":32}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"attn2"},{"from":"attn2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":63,"params":2578400}} {"id":"gen-gqa-3194","family":"gqa","seed":20260716,"spec":"Design a grouped-query-attention encoder for 154-token sequences with 59 output classes: token embedding, 2 GQA block(s) each followed by layer normalization, then a linear head. numHeads must be divisible by numKVHeads. Keep it under 60M params.","observation":"Model: gen-gqa-3194\nComponents:\n - input (input) params={\"shape\":[1,154]}\n - output (output)\nConnections:\n - input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":60000000,"mustContainTypes":["embedding","groupedQueryAttention","layerNorm","linear"],"minComponents":8,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"input","params":{"shape":[1,154]}},{"componentType":"embedding","name":"embed","params":{"numEmbeddings":29913,"embeddingDim":656}},{"componentType":"groupedQueryAttention","name":"attn1","params":{"embedDim":656,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm1","params":{"normalizedShape":[656]}},{"componentType":"groupedQueryAttention","name":"attn2","params":{"embedDim":656,"numHeads":16,"numKVHeads":2}},{"componentType":"layerNorm","name":"norm2","params":{"normalizedShape":[656]}},{"componentType":"linear","name":"head","params":{"inFeatures":656,"outFeatures":59}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"input","to":"embed"},{"from":"embed","to":"attn1"},{"from":"attn1","to":"norm1"},{"from":"norm1","to":"attn2"},{"from":"attn2","to":"norm2"},{"from":"norm2","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":77,"params":19661632}} {"id":"gen-trim-3196","family":"trim","seed":20260716,"spec":"This 4-hidden-layer MLP uses 2092-wide layers and blows a 2M parameter budget. Shrink the widths in place so total params fit the budget, keeping all 4 hidden layers and consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-trim-3196\nComponents:\n - input (input) params={\"shape\":[1,230]}\n - fc1 (linear) params={\"inFeatures\":230,\"outFeatures\":2092}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":2092,\"outFeatures\":2092}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":2092,\"outFeatures\":2092}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":2092,\"outFeatures\":2092}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":2092,\"outFeatures\":29}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":2000000,"mustContainTypes":["linear","relu"],"minComponents":11,"mustReachOutput":true,"maxActions":6,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":230,"outFeatures":233}},{"type":"update_params","name":"fc2","params":{"inFeatures":233,"outFeatures":233}},{"type":"update_params","name":"fc3","params":{"inFeatures":233,"outFeatures":233}},{"type":"update_params","name":"fc4","params":{"inFeatures":233,"outFeatures":233}},{"type":"update_params","name":"head","params":{"inFeatures":233,"outFeatures":29}}],"grade":{"pass":true,"score":88,"params":223214}} {"id":"gen-norm-3197","family":"norm","seed":20260716,"spec":"This 136-feature, 82-class MLP trains unstably. Insert a batchNorm1d after each of the 4 hidden linear layers (between the linear and its activation), with numFeatures matching each layer's output width. Do not rebuild the model from scratch.","observation":"Model: gen-norm-3197\nComponents:\n - input (input) params={\"shape\":[1,136]}\n - fc1 (linear) params={\"inFeatures\":136,\"outFeatures\":340}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":340,\"outFeatures\":407}\n - act2 (relu)\n - fc3 (linear) params={\"inFeatures\":407,\"outFeatures\":450}\n - act3 (relu)\n - fc4 (linear) params={\"inFeatures\":450,\"outFeatures\":207}\n - act4 (relu)\n - head (linear) params={\"inFeatures\":207,\"outFeatures\":82}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> fc3\n - fc3 -> act3\n - act3 -> fc4\n - fc4 -> act4\n - act4 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"mustContainTypes":["batchNorm1d"],"minComponents":15,"mustReachOutput":true,"maxActions":5,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"add_component","componentType":"batchNorm1d","name":"bn1","afterName":"fc1","params":{"numFeatures":340}},{"type":"add_component","componentType":"batchNorm1d","name":"bn2","afterName":"fc2","params":{"numFeatures":407}},{"type":"add_component","componentType":"batchNorm1d","name":"bn3","afterName":"fc3","params":{"numFeatures":450}},{"type":"add_component","componentType":"batchNorm1d","name":"bn4","afterName":"fc4","params":{"numFeatures":207}}],"grade":{"pass":true,"score":100,"params":477894}} {"id":"gen-tower-3198","family":"tower","seed":20260716,"spec":"Design a two-tower retrieval scorer: a 46-feature user input and a 125-feature item input, each through its own 2-layer MLP tower ending at width 375, concatenated and scored by a linear head. Both tower outputs must have identical shape for the merge. Keep it under 10M params.","observation":"Model: gen-tower-3198\nComponents:\n - user_input (input) params={\"shape\":[1,46]}\n - item_input (input) params={\"shape\":[1,125]}\n - output (output)\nConnections:\n - user_input -> output\n - item_input -> output","constraints":{"forbidBlockers":true,"minScore":50,"maxParams":10000000,"mustContainTypes":["linear","relu","concatenate"],"minComponents":11,"mustReachOutput":true},"actions":[{"type":"replace_model","components":[{"componentType":"input","name":"user_input","params":{"shape":[1,46]}},{"componentType":"input","name":"item_input","params":{"shape":[1,125]}},{"componentType":"linear","name":"user_fc1","params":{"inFeatures":46,"outFeatures":375}},{"componentType":"relu","name":"user_act","params":{}},{"componentType":"linear","name":"user_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"linear","name":"item_fc1","params":{"inFeatures":125,"outFeatures":375}},{"componentType":"relu","name":"item_act","params":{}},{"componentType":"linear","name":"item_fc2","params":{"inFeatures":375,"outFeatures":375}},{"componentType":"concatenate","name":"merge","params":{"dim":-1}},{"componentType":"linear","name":"head","params":{"inFeatures":750,"outFeatures":1}},{"componentType":"output","name":"output","params":{}}],"connections":[{"from":"user_input","to":"user_fc1"},{"from":"user_fc1","to":"user_act"},{"from":"user_act","to":"user_fc2"},{"from":"item_input","to":"item_fc1"},{"from":"item_fc1","to":"item_act"},{"from":"item_act","to":"item_fc2"},{"from":"user_fc2","to":"merge"},{"from":"item_fc2","to":"merge"},{"from":"merge","to":"head"},{"from":"head","to":"output"}]}],"grade":{"pass":true,"score":88,"params":346125}} {"id":"gen-grow-3199","family":"grow","seed":20260716,"spec":"This 2-hidden-layer MLP (22-feature input, 7 classes) is far too small for its workload. Widen the hidden layers in place so total params land between 400k and 4M, keeping consistent in/out features. Do not rebuild the model from scratch.","observation":"Model: gen-grow-3199\nComponents:\n - input (input) params={\"shape\":[1,22]}\n - fc1 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act1 (relu)\n - fc2 (linear) params={\"inFeatures\":22,\"outFeatures\":22}\n - act2 (relu)\n - head (linear) params={\"inFeatures\":22,\"outFeatures\":7}\n - output (output)\nConnections:\n - input -> fc1\n - fc1 -> act1\n - act1 -> fc2\n - fc2 -> act2\n - act2 -> head\n - head -> output","constraints":{"forbidBlockers":true,"minScore":50,"minParams":400000,"maxParams":4000000,"mustContainTypes":["linear","relu"],"minComponents":7,"mustReachOutput":true,"maxActions":4,"forbidActionTypes":["replace_model","clear_canvas"]},"actions":[{"type":"update_params","name":"fc1","params":{"inFeatures":22,"outFeatures":699}},{"type":"update_params","name":"fc2","params":{"inFeatures":699,"outFeatures":699}},{"type":"update_params","name":"head","params":{"inFeatures":699,"outFeatures":7}}],"grade":{"pass":true,"score":80,"params":508872}}