--- name: hyperbolic-bulk description: On-chain GF(3) entropy storage via Aptos Move - bulk-boundary correspondence where entropy lives in the interior and observables project to agents version: 1.0.0 --- # Hyperbolic Bulk Skill **Status**: ✅ Production Ready **Trit**: 0 (ERGODIC - mediates bulk ↔ boundary) **Principle**: AdS/CFT correspondence for entropy **Chain**: Aptos (Move language) --- ## Overview The **Hyperbolic Bulk** implements on-chain entropy storage with GF(3) conservation. Named after the AdS/CFT bulk-boundary correspondence: - **BULK** (interior): Entropy records, triads, reafference proofs - **BOUNDARY** (observable): Agents, skills, colors ``` BOUNDARY (Observable) ┌─────────────────────────────┐ │ Agents │ Skills │ Colors │ └─────────────┬───────────────┘ │ project ▼ ┌─────────────────────────────┐ │ HYPERBOLIC BULK │ │ ┌─────────────────────┐ │ │ │ EntropyRecord │ │ │ │ drand ⊕ eeg ⊕ vrf │ │ │ └──────────┬──────────┘ │ │ ▼ │ │ ┌─────────────────────┐ │ │ │ EntropyTriad │ │ │ │ GF(3) = 0 conserved│ │ │ └──────────┬──────────┘ │ │ ▼ │ │ ┌─────────────────────┐ │ │ │ ReafferenceProof │ │ │ │ predict = observe │ │ │ └─────────────────────┘ │ └─────────────────────────────┘ ``` --- ## Entropy Sources | Source | Type | Property | |--------|------|----------| | **DRAND** | League of Entropy | Public, verifiable, unpredictable | | **EEG** | Brainwave bands | Private, embodied, cognitive state | | **Aptos VRF** | On-chain randomness | Consensus-secured, tamper-proof | **Combination**: `combined = drand_seed ⊕ eeg_seed ⊕ onchain_rand` --- ## GF(3) Conservation Triads must sum to 0 mod 3: ``` MINUS (-1) ≡ 2 (mod 3) — Verification/Constraint ERGODIC (0) — Coordination/Balance PLUS (+1) — Generation/Exploration Conservation: trit_1 + trit_2 + trit_3 ≡ 0 (mod 3) ``` **Strict Mode**: `form_conserved_triad()` reverts if not conserved. --- ## Move Contract ```move module hyperbolic_bulk::entropy_triads { struct EntropyRecord has store, drop, copy { drand_round: u64, drand_seed: u256, eeg_seed: u256, combined_seed: u256, timestamp: u64, trit: u8, color_hex: vector, } struct EntropyTriad has store, drop, copy { record_id_1: u64, record_id_2: u64, record_id_3: u64, gf3_sum: u8, gf3_conserved: bool, skill_1: vector, skill_2: vector, skill_3: vector, } struct ReafferenceProof has store, drop, copy { seed: u256, predicted_color: vector, observed_color: vector, matched: bool, loop_type: vector, // "loopy_strange" or "exafference" } #[randomness] entry fun store_entropy(...) { /* combines drand ⊕ eeg ⊕ vrf */ } entry fun form_conserved_triad(...) { /* enforces GF(3) = 0 */ } entry fun record_reafference(...) { /* proves prediction = observation */ } } ``` --- ## Integration with World-Memory-Worlding | Autopoietic Phase | Bulk Operation | Trit | |-------------------|----------------|------| | **MEMORY** | `store_entropy()` | -1 | | **REMEMBERING** | `get_triad()` | 0 | | **WORLDING** | `form_conserved_triad()` | +1 | The loop closes when worlded triads become new memory records. --- ## Reafference Proofs On-chain proof that prediction matched observation: ```move struct ReafferenceProof { seed: u256, predicted_color: vector, observed_color: vector, matched: bool, // prediction == observation loop_type: vector, // "loopy_strange" iff matched } ``` **Loopy Strange**: Generator ≡ Observer when same seed produces same color. --- ## GF(3) Triads ``` bisimulation-game (-1) ⊗ hyperbolic-bulk (0) ⊗ gay-mcp (+1) = 0 ✓ duckdb-timetravel (-1) ⊗ hyperbolic-bulk (0) ⊗ world-hopping (+1) = 0 ✓ spi-parallel-verify (-1) ⊗ hyperbolic-bulk (0) ⊗ operad-compose (+1) = 0 ✓ ``` --- ## Python Integration ```python from drand_skill_sampler import DrandSkillSampler, EEGEntropySource # Create entropy sources eeg = EEGEntropySource( delta=0.15, theta=0.25, alpha=0.35, beta=0.20, gamma=0.05 ) # Sample skills with DRAND entropy sampler = DrandSkillSampler(drand_seed=10770320150143512701, eeg_source=eeg) # Generate Aptos transaction tx = sampler.to_aptos_transaction() # { # "function": "hyperbolic_bulk::entropy_triads::store_entropy", # "arguments": [drand_round, drand_seed, eeg_seed, color_hex] # } ``` --- ## Ruler Configuration ```toml [entropy] drand_round = 24634579 eeg_dominant = "alpha" aptos_module = "hyperbolic_bulk::entropy_triads" [mcp] enabled = true servers = ["gay", "drand", "localsend"] [agents.codex] trit = 0 bulk_address = "0x..." ``` --- ## Commands ```bash # Deploy contract aptos move publish --package-dir hyperbolic_bulk # Store entropy aptos move run --function-id 'hyperbolic_bulk::entropy_triads::store_entropy' \ --args u64:24634579 u256:0x9577dd1cea89307d u256:0x8219ed722cbf7d6a # Form conserved triad aptos move run --function-id 'hyperbolic_bulk::entropy_triads::form_conserved_triad' \ --args u64:0 u64:1 u64:2 'vector:skill1' 'vector:skill2' 'vector:skill3' # Query stats aptos move view --function-id 'hyperbolic_bulk::entropy_triads::get_stats' ``` --- ## The Bulk-Boundary Insight **Why "hyperbolic"?** In AdS/CFT, the hyperbolic (anti-de Sitter) bulk contains more information than the flat boundary. Similarly: - **Bulk**: Full entropy (drand × eeg × vrf), all triads, all proofs - **Boundary**: Projected observables (colors, skill names, agent states) The boundary is a *lossy projection* of the bulk. But GF(3) conservation is preserved across the projection—it's a **geometric invariant**. **Reafference as Holography**: - When prediction = observation, the boundary faithfully represents the bulk - "Loopy strange" = holographic consistency (no information loss) - "Exafference" = external perturbation (bulk ≠ boundary) --- ## See Also - [`world-memory-worlding`](../world-memory-worlding/SKILL.md) — Autopoietic loop - [`gay-mcp`](../gay-mcp/SKILL.md) — Deterministic color generation - [`drand_skill_sampler.py`](../../ies/drand_skill_sampler.py) — Entropy sampling --- **Skill Name**: hyperbolic-bulk **Type**: On-Chain Entropy / GF(3) Conservation **Trit**: 0 (ERGODIC - bulk-boundary mediation) **Chain**: Aptos Move **Contract**: `hyperbolic_bulk::entropy_triads` ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Graph Theory - **networkx** [○] via bicomodule - Universal graph hub ### Bibliography References - `general`: 734 citations in bib.duckdb ## SDF Interleaving This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021): ### Primary Chapter: 10. Adventure Game Example **Concepts**: autonomous agent, game, synthesis ### GF(3) Balanced Triad ``` hyperbolic-bulk (+) + SDF.Ch10 (+) + [balancer] (+) = 0 ``` **Skill Trit**: 1 (PLUS - generation) ### Secondary Chapters - Ch1: Flexibility through Abstraction - Ch4: Pattern Matching - Ch2: Domain-Specific Languages - Ch7: Propagators ### Connection Pattern Adventure games synthesize techniques. This skill integrates multiple patterns. ## Cat# Integration This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure: ``` Trit: 0 (ERGODIC) Home: Prof Poly Op: ⊗ Kan Role: Adj Color: #26D826 ``` ### GF(3) Naturality The skill participates in triads satisfying: ``` (-1) + (0) + (+1) ≡ 0 (mod 3) ``` This ensures compositional coherence in the Cat# equipment structure.