[
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "James Kirkpatrick",
      "Razvan Pascanu",
      "Neil Rabinowitz",
      "Joel Veness"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "James Kirkpatrick; Razvan Pascanu; Neil Rabinowitz; Joel Veness (2017). Overcoming catastrophic forgetting in neural networks. PNAS. https://doi.org/10.1073/pnas.1611835114",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://doi.org/10.1073/pnas.1611835114"
      }
    ],
    "extraction_completeness": "full",
    "extraction_confidence": "high",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [
      "L(\u03b8)=L_B(\u03b8)+(\u03bb/2)\u03a3_i F_i(\u03b8_i-\u03b8_i*)^2",
      "F_i=E[(\u2202/\u2202\u03b8_i log p(y|x,\u03b8*))^2]"
    ],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Seed canonical method source; deeply inspected to extract equations, assumptions, and procedures for downstream methodology.",
    "theorem_proof_scaffolds": [],
    "title": "Overcoming catastrophic forgetting in neural networks",
    "url": "https://doi.org/10.1073/pnas.1611835114",
    "year": 2017
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Zhizhong Li",
      "Derek Hoiem"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Zhizhong Li; Derek Hoiem (2016). Learning Without Forgetting. ECCV. https://doi.org/10.1007/978-3-319-46493-0_37",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://doi.org/10.1007/978-3-319-46493-0_37"
      }
    ],
    "extraction_completeness": "full",
    "extraction_confidence": "high",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [
      "L_total=L_new+\u03bb_o L_distill",
      "L_distill=-\u03a3_c y_c^{old,T} log y_c^{new,T}"
    ],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Seed canonical method source; deeply inspected to extract equations, assumptions, and procedures for downstream methodology.",
    "theorem_proof_scaffolds": [],
    "title": "Learning Without Forgetting",
    "url": "https://doi.org/10.1007/978-3-319-46493-0_37",
    "year": 2016
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Andrei A. Rusu",
      "Neil C. Rabinowitz",
      "Guillaume Desjardins",
      "Hubert Soyer"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Andrei A. Rusu; Neil C. Rabinowitz; Guillaume Desjardins; Hubert Soyer (2016). Progressive Neural Networks. arXiv. https://arxiv.org/abs/1606.04671",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1606.04671"
      }
    ],
    "extraction_completeness": "full",
    "extraction_confidence": "high",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [
      "h_i^(k)=f(W_i^(k)h_{i-1}^(k)+\u03a3_{j<k}U_i^(k:j)h_{i-1}^(j))"
    ],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Seed canonical method source; deeply inspected to extract equations, assumptions, and procedures for downstream methodology.",
    "theorem_proof_scaffolds": [],
    "title": "Progressive Neural Networks",
    "url": "https://arxiv.org/abs/1606.04671",
    "year": 2016
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "David Lopez-Paz",
      "Marc'Aurelio Ranzato"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "David Lopez-Paz; Marc'Aurelio Ranzato (2017). Gradient Episodic Memory for Continual Learning. NeurIPS. https://arxiv.org/abs/1706.08840",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1706.08840"
      }
    ],
    "extraction_completeness": "full",
    "extraction_confidence": "high",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [
      "min ||g~-g||_2^2 s.t. <g~,g_k>\u22650 for k<t"
    ],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Seed canonical method source; deeply inspected to extract equations, assumptions, and procedures for downstream methodology.",
    "theorem_proof_scaffolds": [],
    "title": "Gradient Episodic Memory for Continual Learning",
    "url": "https://arxiv.org/abs/1706.08840",
    "year": 2017
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Arslan Chaudhry",
      "Marc'Aurelio Ranzato",
      "Marcus Rohrbach",
      "Mohammad Elhoseiny"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Arslan Chaudhry; Marc'Aurelio Ranzato; Marcus Rohrbach; Mohammad Elhoseiny (2019). Efficient Lifelong Learning with A-GEM. ICLR. https://arxiv.org/abs/1812.00420",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1812.00420"
      }
    ],
    "extraction_completeness": "full",
    "extraction_confidence": "high",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [
      "if g^Tg_ref<0 then g~=g-((g^Tg_ref)/(g_ref^Tg_ref))g_ref"
    ],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Seed canonical method source; deeply inspected to extract equations, assumptions, and procedures for downstream methodology.",
    "theorem_proof_scaffolds": [],
    "title": "Efficient Lifelong Learning with A-GEM",
    "url": "https://arxiv.org/abs/1812.00420",
    "year": 2019
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Sylvestre-Alvise Rebuffi",
      "Alexander Kolesnikov",
      "Georg Sperl",
      "Christoph H. Lampert"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Sylvestre-Alvise Rebuffi; Alexander Kolesnikov; Georg Sperl; Christoph H. Lampert (2017). iCaRL: Incremental Classifier and Representation Learning. CVPR. https://doi.org/10.1109/CVPR.2017.587",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://doi.org/10.1109/CVPR.2017.587"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "medium",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "iCaRL: Incremental Classifier and Representation Learning",
    "url": "https://doi.org/10.1109/CVPR.2017.587",
    "year": 2017
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Friedemann Zenke",
      "Ben Poole",
      "Surya Ganguli"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Friedemann Zenke; Ben Poole; Surya Ganguli (2017). Continual Learning Through Synaptic Intelligence. ICML. https://proceedings.mlr.press/v70/zenke17a.html",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://proceedings.mlr.press/v70/zenke17a.html"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "medium",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Continual Learning Through Synaptic Intelligence",
    "url": "https://proceedings.mlr.press/v70/zenke17a.html",
    "year": 2017
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Rahaf Aljundi",
      "Francesco Babiloni",
      "Mohamed Elhoseiny",
      "Marcus Rohrbach"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Rahaf Aljundi; Francesco Babiloni; Mohamed Elhoseiny; Marcus Rohrbach (2018). Memory Aware Synapses: Learning what (not) to forget. ECCV. https://arxiv.org/abs/1711.09601",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1711.09601"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "medium",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Memory Aware Synapses: Learning what (not) to forget",
    "url": "https://arxiv.org/abs/1711.09601",
    "year": 2018
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Hanul Shin",
      "Jung Kwon Lee",
      "Jaehong Kim",
      "Jiwon Kim"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Hanul Shin; Jung Kwon Lee; Jaehong Kim; Jiwon Kim (2017). Continual Learning with Deep Generative Replay. NeurIPS. https://arxiv.org/abs/1705.08690",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1705.08690"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "medium",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Continual Learning with Deep Generative Replay",
    "url": "https://arxiv.org/abs/1705.08690",
    "year": 2017
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "David Rolnick",
      "Aravind Ahuja",
      "Jonathan Schwarz",
      "Timothy Lillicrap"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "David Rolnick; Aravind Ahuja; Jonathan Schwarz; Timothy Lillicrap (2019). Experience Replay for Continual Learning. NeurIPS. https://arxiv.org/abs/1811.11682",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://arxiv.org/abs/1811.11682"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "medium",
    "future_work": [
      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
    "key_equations": [],
    "limitations": [
      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
    "notation_seeds": [
      "\u03b8 parameters",
      "M memory buffer"
    ],
    "parameters": [
      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
    "procedures": [
      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
    "reusable_limitations": [
      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Experience Replay for Continual Learning",
    "url": "https://arxiv.org/abs/1811.11682",
    "year": 2019
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Arslan Chaudhry",
      "Puneet K. Dokania",
      "Thalaiyasingam Ajanthan",
      "Philip H. S. Torr"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Arslan Chaudhry; Puneet K. Dokania; Thalaiyasingam Ajanthan; Philip H. S. Torr (2018). Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. ECCV. https://doi.org/10.1007/978-3-030-01252-6_33",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
    "contradiction_pairs": [],
    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Rahaf Aljundi",
      "Marcus Rohrbach",
      "Tinne Tuytelaars"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Rahaf Aljundi; Marcus Rohrbach; Tinne Tuytelaars (2019). Online Continual Learning with Maximally Interfered Retrieval. NeurIPS. https://arxiv.org/abs/1908.04742",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
    ],
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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    "title": "Online Continual Learning with Maximally Interfered Retrieval",
    "url": "https://arxiv.org/abs/1908.04742",
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Matthew Riemer",
      "Ignacio Cases",
      "Robert Ajemian",
      "Miao Liu"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    ],
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      "Finite memory and sequential non-iid tasks are central constraints."
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      "Matteo Boschini",
      "Angelo Porrello",
      "Davide Abati"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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    ],
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    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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    "title": "Dark Experience for General Continual Learning: a Strong, Simple Baseline",
    "url": "https://arxiv.org/abs/2004.07211",
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Ameya Prabhu",
      "Philip H. S. Torr",
      "Puneet K. Dokania"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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    ],
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    "theorem_proof_scaffolds": [],
    "title": "GDumb: A Simple Approach that Questions Our Progress in Continual Learning",
    "url": "https://doi.org/10.1007/978-3-030-58536-5_31",
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      "Eli Verwimp",
      "Matthias De Lange",
      "Tinne Tuytelaars"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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    "theorem_proof_scaffolds": [],
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    "url": "https://doi.org/10.1109/ICCV48922.2021.00925",
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  },
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Jongseok Lee",
      "Donggeon Kim",
      "Sung Ju Hwang"
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      "Compared to replay/regularization baselines where reported."
    ],
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    ],
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    ],
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Hava T. Siegelmann",
      "Andreas S. Tolias"
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      "Compared to replay/regularization baselines where reported."
    ],
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    ],
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      }
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    "url": "https://doi.org/10.1038/s41467-020-17866-2",
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    ],
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  },
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    ],
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      "Rahaf Aljundi",
      "Marc Masana",
      "Sarah Parisot"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
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    "source_type": "paper",
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    "theorem_proof_scaffolds": [],
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      "Andrea Cossu",
      "Hamed Hemati"
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Alessio Ragno",
      "Roberto Capobianco"
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      "Finite memory and sequential non-iid tasks are central constraints."
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      "Manabu Okumura"
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      "Compared to replay/regularization baselines where reported."
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      "Finite memory and sequential non-iid tasks are central constraints."
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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      "Finite memory and sequential non-iid tasks are central constraints."
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
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      "Protocol sensitivity across scenarios."
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    "theorem_proof_scaffolds": [],
    "title": "Saliency-driven Experience Replay for Continual Learning",
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    "year": 2024
  },
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      "Jack Julian",
      "Yun Sing Koh",
      "Albert Bifet"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Jack Julian; Yun Sing Koh; Albert Bifet (2024). Sketch-Based Replay Projection for Continual Learning. KDD. https://doi.org/10.1145/3637528.3671714",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
    ],
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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      "Provides evidence or method relevant to continual-learning memory design."
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    "year": 2024
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  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
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      "Shuncheng Liu",
      "Haitian Chen"
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      "Compared to replay/regularization baselines where reported."
    ],
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    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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      "Provides evidence or method relevant to continual-learning memory design."
    ],
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      "Sequential task-stream training with bounded memory/replay or consolidation policy."
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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      "Protocol sensitivity across scenarios."
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    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Continual Trajectory Prediction with Uncertainty-Aware Generative Memory Replay",
    "url": "https://doi.org/10.1109/ICDM58522.2023.00116",
    "year": 2023
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    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Jiafu Hao",
      "Son Lam Phung",
      "Yang Di"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Jiafu Hao; Son Lam Phung; Yang Di (2023). Enhanced Experience Replay for Class Incremental Continual Learning. DICTA. https://doi.org/10.1109/DICTA60407.2023.00043",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
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        "atom_type": "claim",
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        "content": "Source contributes to continual-learning memory tradeoff evidence",
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    ],
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      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
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      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
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      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
    "provenance_notes": [
      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Enhanced Experience Replay for Class Incremental Continual Learning",
    "url": "https://doi.org/10.1109/DICTA60407.2023.00043",
    "year": 2023
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Jack Good",
      "Jimit Majmudar",
      "Christophe Dupuy"
    ],
    "baseline_details": [
      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Jack Good; Jimit Majmudar; Christophe Dupuy (2023). Coordinated Replay Sample Selection for Continual Federated Learning. EMNLP Industry. https://doi.org/10.18653/v1/2023.emnlp-industry.32",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
    "comparator_lineage": [
      "Replay vs regularization vs architectural isolation families."
    ],
    "conclusions": [
      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
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      {
        "atom_type": "claim",
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        "content": "Source contributes to continual-learning memory tradeoff evidence",
        "locator": null,
        "source_ref": "https://doi.org/10.18653/v1/2023.emnlp-industry.32"
      }
    ],
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      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
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      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
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    ],
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      "Memory size M",
      "Regularization/selection hyperparameters"
    ],
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      "Sequential task-stream training with bounded memory/replay or consolidation policy."
    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Coordinated Replay Sample Selection for Continual Federated Learning",
    "url": "https://doi.org/10.18653/v1/2023.emnlp-industry.32",
    "year": 2023
  },
  {
    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Xingyu Li",
      "Bo Tang"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Xingyu Li; Bo Tang (2024). MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization. IJCNN. https://doi.org/10.1109/IJCNN60899.2024.10650091",
    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
    ],
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    "contributions": [
      "Provides evidence or method relevant to continual-learning memory design."
    ],
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      }
    ],
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      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
    ],
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      "Often benchmark-specific; system-level latency/energy evidence is limited."
    ],
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      "Memory size M",
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    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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      "Protocol sensitivity across scenarios."
    ],
    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization",
    "url": "https://doi.org/10.1109/IJCNN60899.2024.10650091",
    "year": 2024
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    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Elham Karam Serria",
      "Mishal Fatima Minhas",
      "Falah Awwad"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
    "citation": "Elham Karam Serria; Mishal Fatima Minhas; Falah Awwad (2025). Prototype Replay: Memory-Efficient Continual Learning via Class-Conditioned Centroids. ICM. https://doi.org/10.1109/ICM66518.2025.11321318",
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      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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      "Replay vs regularization vs architectural isolation families."
    ],
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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    ],
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      }
    ],
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      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
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    ],
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
    ],
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    ],
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    "theorem_proof_scaffolds": [],
    "title": "Prototype Replay: Memory-Efficient Continual Learning via Class-Conditioned Centroids",
    "url": "https://doi.org/10.1109/ICM66518.2025.11321318",
    "year": 2025
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    "assumptions": [
      "Finite memory and sequential non-iid tasks are central constraints."
    ],
    "authors": [
      "Arash Khajooeinejad",
      "Masoumeh Chapariniya",
      "Teodora Vukovic"
    ],
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      "Compared to replay/regularization baselines where reported."
    ],
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    "claims": [
      "Memory policy and update constraints materially affect forgetting/adaptation tradeoffs."
    ],
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    ],
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      "Useful comparator or design reference for entropy-aware memory scheduling studies."
    ],
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      "Provides evidence or method relevant to continual-learning memory design."
    ],
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      "Sequential task-stream training with bounded memory/replay or consolidation policy."
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
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    "theorem_proof_scaffolds": [],
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  {
    "assumptions": [
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    "authors": [
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      "Jinge Wu",
      "Vaanathi Sundaresan"
    ],
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      "Compared to replay/regularization baselines where reported."
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    "claims": [
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      "Need hardware-aware and uncertainty-aware continual-learning evaluations."
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      "Often benchmark-specific; system-level latency/energy evidence is limited."
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      "Regularization/selection hyperparameters"
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      "Sequential task-stream training with bounded memory/replay or consolidation policy."
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      "Canonical URL resolved; full-text/metadata reviewed where accessible."
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      "Protocol sensitivity across scenarios."
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    "source_type": "paper",
    "summary": "Replay/regularization/modular continual-learning source relevant to memory-efficiency and stability-plasticity tradeoffs.",
    "theorem_proof_scaffolds": [],
    "title": "Attentive Latent Replay for Continual Learning in Pathology",
    "url": "https://doi.org/10.1109/ISBI60581.2025.10980701",
    "year": 2025
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]