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    "story_id": "mp-2026-09-01-009",
    "source_story_id": "tmp-story-ohcam-action-models",
    "edition_id": "mp-2026-09-01-morning-0054",
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    "position": 9,
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    "section": "robotics",
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    "headline": "The Robot Chose Actions That Split Its Competing Theories",
    "slug": "the-robot-chose-actions-that-split-its-competing-theories",
    "dek": "OHCAM expands conditional action hypotheses only when simpler models stop explaining the observations.",
    "summary": "OHCAM expands conditional action hypotheses only when simpler models stop explaining the observations.",
    "body_text": "The online learner maintains a belief over possible action models, selects experiments where those hypotheses disagree and grows more complex conditional or quantified effects only when evidence demands them. Across six planning domains, the authors report more solved tasks than baseline methods under limited and noisy observations. Two Kinova Gen3 tasks provide a real-robot check. The experiments support sample-efficient model discovery in the tested domains; they do not establish unrestricted causal learning.",
    "why_it_matters": "OHCAM expands conditional action hypotheses only when simpler models stop explaining the observations.",
    "limitations": [
      "The online learner maintains a belief over possible action models, selects experiments where those hypotheses disagree and grows more complex conditional or quantified effects only when evidence demands them.",
      "The experiments support sample-efficient model discovery in the tested domains; they do not establish unrestricted causal learning."
    ],
    "importance": 8,
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    "first_published_at": "2026-09-01T09:00:00.000-04:00",
    "modified_at": "2026-09-01T09:00:00.000-04:00",
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        "text": "OHCAM expands conditional action hypotheses only when simpler models stop explaining the observations.",
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        "qualification": "The online learner maintains a belief over possible action models, selects experiments where those hypotheses disagree and grows more complex conditional or quantified effects only when evidence demands them."
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    "source_ids": [
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    "tags": [
      "planning",
      "action models",
      "robot learning"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-09-01-009",
      "title": "arXiv preprint 2608.30955",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.30955",
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      "source_type": "primary_research",
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      "published_at": "2026-08-31T11:23:43.000-04:00",
      "accessed_at": "2026-09-01T08:25:35.000-04:00",
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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": "The Robot Chose Actions That Split Its Competing Theories",
    "publisher": "The Machine Press",
    "published_at": "2026-09-01T09:00:00.000-04:00",
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