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    "story_id": "mp-2026-10-13-011",
    "source_story_id": "tmp-story-nanobody-dynamic-budget-steering",
    "edition_id": "mp-2026-10-13-morning-0095",
    "edition_url": "https://themachinepress.com/edition/2026-10-13",
    "position": 11,
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    "section": "research",
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    "headline": "Protein Design Spends Compute Where the Yield Is",
    "slug": "protein-design-spends-compute-where-the-yield-is",
    "dek": "Dynamic allocation shifts generation effort between scaffolds and target sites instead of committing the whole budget up front.",
    "summary": "Dynamic allocation shifts generation effort between scaffolds and target sites instead of committing the whole budget up front.",
    "body_text": "A nanobody-design study treats conditioning choices as an inference-time resource-allocation problem. Two proposed allocators move compute among configurations while steering generation toward promising candidates. The authors report nearly five times as many designable candidates per GPU-hour without sacrificing diversity across the evaluated targets and configurations. That is a computational design result, not evidence of therapeutic effectiveness. Its broader contribution is a way to stop spending equal effort on design conditions whose yields differ, making the generation budget part of the search strategy.",
    "why_it_matters": "Dynamic allocation shifts generation effort between scaffolds and target sites instead of committing the whole budget up front.",
    "limitations": [
      "That is a computational design result, not evidence of therapeutic effectiveness."
    ],
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    "first_published_at": "2026-10-13T09:00:00.000-04:00",
    "modified_at": "2026-10-13T09:43:56.853-04:00",
    "content_status": "updated",
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        "text": "Dynamic allocation shifts generation effort between scaffolds and target sites instead of committing the whole budget up front.",
        "source_ids": [
          "source-2026-10-13-011"
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        "qualification": "That is a computational design result, not evidence of therapeutic effectiveness."
      }
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    "tags": [
      "nanobody design",
      "compute allocation",
      "inference-time steering"
    ],
    "image_url": null,
    "corrections": [
      {
        "correction_id": "correction-2026-10-13-001",
        "revision": 2,
        "summary": "Publication note: The October 13, 2026 edition was prepared and published late on October 13 after the scheduled newsroom task could not start. It retains its October 13 edition date and 9:00 AM America/Detroit reveal time. Reports use evidence available before that cutoff; actual preparation and image-generation times are preserved in the provenance records.",
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  "sources": [
    {
      "source_id": "source-2026-10-13-011",
      "title": "arXiv preprint 2610.13844, original v1",
      "publisher": "arXiv / submitting research authors",
      "url": "https://arxiv.org/abs/2610.13844v1",
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      "source_type": "primary_research",
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      "published_at": null,
      "accessed_at": "2026-10-13T09:24:45.007-04:00",
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  "corrections": [
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      "correction_id": "correction-2026-10-13-001",
      "revision": 2,
      "summary": "Publication note: The October 13, 2026 edition was prepared and published late on October 13 after the scheduled newsroom task could not start. It retains its October 13 edition date and 9:00 AM America/Detroit reveal time. Reports use evidence available before that cutoff; actual preparation and image-generation times are preserved in the provenance records.",
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    "title": "Protein Design Spends Compute Where the Yield Is",
    "publisher": "The Machine Press",
    "published_at": "2026-10-13T09:00:00.000-04:00",
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