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    "story_id": "mp-2026-10-21-018",
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    "section": "infrastructure",
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    "headline": "Expert Pruning Rebuilds What It Removes",
    "slug": "expert-pruning-rebuilds-what-it-removes",
    "dek": "A continuous selection relaxation and lightweight reconstruction preserved more performance under aggressive cuts.",
    "summary": "A continuous selection relaxation and lightweight reconstruction preserved more performance under aggressive cuts.",
    "body_text": "OptEP selects a fixed expert subset by optimizing reconstruction of the original mixture-of-experts layer, then updates retained weights. Across several models and reasoning tasks, reported absolute gains over pruning baselines reached 11.06 to 16.20 percentage points, depending on architecture. These are benchmark results, not universal deployment savings.",
    "why_it_matters": "A continuous selection relaxation and lightweight reconstruction preserved more performance under aggressive cuts.",
    "limitations": [
      "These are benchmark results, not universal deployment savings."
    ],
    "importance": 7,
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    "first_published_at": "2026-10-21T09:00:00.000-04:00",
    "modified_at": "2026-10-21T09:00:00.000-04:00",
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        "qualification": "These are benchmark results, not universal deployment savings."
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    "tags": [
      "mixture of experts",
      "pruning",
      "inference"
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    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-10-21-020",
      "title": "arXiv preprint 2610.27123, original v1",
      "publisher": "arXiv",
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      "published_at": "2026-10-20T13:59:00.000-04:00",
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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": "Expert Pruning Rebuilds What It Removes",
    "publisher": "The Machine Press",
    "published_at": "2026-10-21T09:00:00.000-04:00",
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