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    "story_id": "mp-2026-09-16-004",
    "source_story_id": "tmp-story-frozen-base-error-correction",
    "edition_id": "mp-2026-09-16-morning-0069",
    "edition_url": "https://themachinepress.com/edition/2026-09-16",
    "position": 4,
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    "section": "frontier-models",
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    "headline": "A Small Corrector Fixed Errors Without Rewriting the Base Model",
    "slug": "a-small-corrector-fixed-errors-without-rewriting-the-base-model",
    "dek": "A 34-million-parameter module corrected 53.3% of tested errors while the matched evaluations showed no base-capability loss.",
    "summary": "A 34-million-parameter module corrected 53.3% of tested errors while the matched evaluations showed no base-capability loss.",
    "body_text": "CRN v2 leaves a 4.65-billion-parameter language model frozen and learns a roughly 34-million-parameter logit correction layer on 83,400 pairs. On a 60-question domain exam it corrected 53.3% of base-model errors; a reworded version reached 43.3%. A matched LoRA baseline corrected more errors but lost 30% to 75% on the same small capability checks. Lowering the preservation penalty sharply reduced correction. The experiment supports frozen-base adjustment as a tradeoff, but its 60-question domain exam and small benchmark samples are not evidence of broad capability preservation.",
    "why_it_matters": "A 34-million-parameter module corrected 53.3% of tested errors while the matched evaluations showed no base-capability loss.",
    "limitations": [
      "The experiment supports frozen-base adjustment as a tradeoff, but its 60-question domain exam and small benchmark samples are not evidence of broad capability preservation."
    ],
    "importance": 8,
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    "first_published_at": "2026-09-16T09:00:00.000-04:00",
    "modified_at": "2026-09-16T09:00:00.000-04:00",
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        "text": "A 34-million-parameter module corrected 53.3% of tested errors while the matched evaluations showed no base-capability loss.",
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        "qualification": "The experiment supports frozen-base adjustment as a tradeoff, but its 60-question domain exam and small benchmark samples are not evidence of broad capability preservation."
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    "source_ids": [
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    "tags": [
      "model editing",
      "capability preservation",
      "fine-tuning"
    ],
    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-09-16-004",
      "title": "arXiv preprint 2609.16145",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.16145",
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      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-09-14T20:00:00.000-04:00",
      "accessed_at": "2026-09-16T08:18:00.000-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "A Small Corrector Fixed Errors Without Rewriting the Base Model",
    "publisher": "The Machine Press",
    "published_at": "2026-09-16T09:00:00.000-04:00",
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