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    "story_id": "mp-2026-08-23-006",
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    "headline": "Compression Hid the Knowledge It Lost",
    "slug": "compression-hid-the-knowledge-it-lost",
    "dek": "Aggregate accuracy and bias scores concealed subgroup shifts and confident errors across eleven compression methods.",
    "summary": "Aggregate accuracy and bias scores concealed subgroup shifts and confident errors across eleven compression methods.",
    "body_text": "Researchers evaluated three language models across eleven compression methods and found that compressed systems disproportionately lost head knowledge relative to tail knowledge while often remaining confident about newly incorrect answers. Stable aggregate bias scores also masked opposing movements across demographic subgroups, arguing for granular deployment audits rather than a single perplexity, accuracy or bias number.",
    "why_it_matters": "Aggregate accuracy and bias scores concealed subgroup shifts and confident errors across eleven compression methods.",
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    "importance": 8,
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    "first_published_at": "2026-08-23T09:00:00.000-04:00",
    "modified_at": "2026-08-23T09:00:00.000-04:00",
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    "tags": [
      "model compression",
      "bias",
      "knowledge retention"
    ],
    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-08-23-006",
      "title": "arXiv preprint 2608.19670",
      "publisher": "arXiv",
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      "published_at": "2026-08-20T02:06:14.000-04:00",
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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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    "title": "Compression Hid the Knowledge It Lost",
    "publisher": "The Machine Press",
    "published_at": "2026-08-23T09:00:00.000-04:00",
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