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  "story": {
    "story_id": "mp-2026-11-02-002",
    "source_story_id": "tmp-feature-roboarchitect-harness",
    "edition_id": "mp-2026-11-02-morning-0115",
    "edition_url": "https://themachinepress.com/edition/2026-11-02",
    "position": 2,
    "story_type": "secondary",
    "section": "robotics",
    "editorial_classification": "editorial",
    "headline": "The Policy Stays Frozen. The Harness Learns.",
    "slug": "the-policy-stays-frozen-the-harness-learns",
    "dek": "RoboArchitect let a coding model revise the control harness around a frozen robot policy, raising reported success from 9.7 to 43.3 percent on unseen scenes.",
    "summary": "RoboArchitect let a coding model revise the control harness around a frozen robot policy, raising reported success from 9.7 to 43.3 percent on unseen scenes.",
    "body_text": "A robot policy does not act alone: surrounding control code decides when to query it, call a vision tool or command the arm directly. RoboArchitect automates the design of that harness. A coding model watches rollouts, diagnoses failures and iteratively revises the controller around an unchanged policy; evaluation then moves to held-out scenes and tasks. Across the reported benchmarks and policies, average success rose from 9.7 to 43.3 percent on unseen scenes and from 12.7 to 40.2 percent on unseen tasks. The evolved harnesses discovered tactics such as lifting a jammed arm clear, approaching around obstacles and retaining useful grasp depths in persistent memory. The evidence is benchmark-based, and it does not establish that unconstrained self-modification is safe or robust on arbitrary hardware.",
    "why_it_matters": "RoboArchitect let a coding model revise the control harness around a frozen robot policy, raising reported success from 9.7 to 43.3 percent on unseen scenes.",
    "limitations": [
      "A robot policy does not act alone: surrounding control code decides when to query it, call a vision tool or command the arm directly.",
      "The evidence is benchmark-based, and it does not establish that unconstrained self-modification is safe or robust on arbitrary hardware."
    ],
    "importance": 10,
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    "first_published_at": "2026-11-02T09:00:00.000-05:00",
    "modified_at": "2026-11-02T09:00:00.000-05:00",
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        "text": "RoboArchitect let a coding model revise the control harness around a frozen robot policy, raising reported success from 9.7 to 43.3 percent on unseen scenes.",
        "source_ids": [
          "source-2026-11-02-002"
        ],
        "qualification": "A robot policy does not act alone: surrounding control code decides when to query it, call a vision tool or command the arm directly."
      }
    ],
    "source_ids": [
      "source-2026-11-02-002"
    ],
    "tags": [
      "robotics",
      "agent harnesses",
      "persistent memory",
      "policy adaptation"
    ],
    "image_url": "https://themachinepress.com/issues/2026-11-02/feature-roboarchitect.png",
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-11-02-002",
      "title": "arXiv preprint 2610.42141, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.42141v1",
      "canonical_url": "https://arxiv.org/abs/2610.42141v1",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-10-30T13:58:48.000-04:00",
      "accessed_at": "2026-11-02T08:25:20.000-05:00",
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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": "The Policy Stays Frozen. The Harness Learns.",
    "publisher": "The Machine Press",
    "published_at": "2026-11-02T09:00:00.000-05:00",
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