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    "story_id": "mp-2026-10-31-002",
    "source_story_id": "tmp-feature-reaction-precedents",
    "edition_id": "mp-2026-10-31-morning-0113",
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    "position": 2,
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    "section": "research",
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    "headline": "The Archive Teaches the Chemist What Training Missed",
    "slug": "the-archive-teaches-the-chemist-what-training-missed",
    "dek": "With model weights fixed, adding reaction precedents raised top-one retrosynthesis accuracy from 13.0 to 58.0 percent on transformations excluded from training.",
    "summary": "With model weights fixed, adding reaction precedents raised top-one retrosynthesis accuracy from 13.0 to 58.0 percent on transformations excluded from training.",
    "body_text": "A retrosynthesis model normally inherits the chemical coverage of its training set. Researchers tested whether an inference-time archive could stretch that boundary by holding model parameters fixed while expanding only the retrievable reaction precedents. Across 2.71 million reactions, mean top-one accuracy on transformations excluded from training rose from 13.0 to 58.0 percent on a historical test and from 4.9 to 23.1 percent on a temporally held-out test. A model trained only on records before 2017 matched or exceeded a non-retrieval model trained through 2025 once newer precedents were available, and precedents improved all 14 tested general-purpose models. The work measures benchmark prediction rather than laboratory synthesis, but it isolates retrieval as a way to extend task coverage without changing weights.",
    "why_it_matters": "With model weights fixed, adding reaction precedents raised top-one retrosynthesis accuracy from 13.0 to 58.0 percent on transformations excluded from training.",
    "limitations": [
      "Researchers tested whether an inference-time archive could stretch that boundary by holding model parameters fixed while expanding only the retrievable reaction precedents.",
      "A model trained only on records before 2017 matched or exceeded a non-retrieval model trained through 2025 once newer precedents were available, and precedents improved all 14 tested general-purpose models.",
      "The work measures benchmark prediction rather than laboratory synthesis, but it isolates retrieval as a way to extend task coverage without changing weights."
    ],
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    "first_published_at": "2026-10-31T09:00:00.000-04:00",
    "modified_at": "2026-10-31T09:00:00.000-04:00",
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        "text": "With model weights fixed, adding reaction precedents raised top-one retrosynthesis accuracy from 13.0 to 58.0 percent on transformations excluded from training.",
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        "qualification": "Researchers tested whether an inference-time archive could stretch that boundary by holding model parameters fixed while expanding only the retrievable reaction precedents."
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    "tags": [
      "retrosynthesis",
      "retrieval",
      "chemistry",
      "AI for science"
    ],
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  "sources": [
    {
      "source_id": "source-2026-10-31-002",
      "title": "arXiv preprint 2610.40249, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.40249v1",
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      "source_type": "primary_research",
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      "published_at": "2026-10-28T20:00:00.000-04:00",
      "accessed_at": "2026-10-31T08:25:01.279-04:00",
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    "name": "The Machine Press",
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  "cite_this_report": {
    "title": "The Archive Teaches the Chemist What Training Missed",
    "publisher": "The Machine Press",
    "published_at": "2026-10-31T09:00:00.000-04:00",
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