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    "section": "robotics",
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    "headline": "A Robot Kept the World Model’s Insight, Not Its Bulk",
    "slug": "a-robot-kept-the-world-model-s-insight-not-its-bulk",
    "dek": "Feature alignment transferred world-model representations into a compact action policy.",
    "summary": "Feature alignment transferred world-model representations into a compact action policy.",
    "body_text": "A robot-learning study used a frozen world model to produce training-frame features, then taught a vision-language-action policy to match those cached features. The world model was absent from the deployed policy, leaving the same inference architecture as the baseline. The authors report a 32-millisecond runtime and 1.86-gigabyte footprint on an RTX 5090, with gains attributed to the learned representation rather than added deployment capacity. This demonstrates a training approach on the studied tasks and hardware, not universal physical grounding.",
    "why_it_matters": "Feature alignment transferred world-model representations into a compact action policy.",
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        "qualification": "This demonstrates a training approach on the studied tasks and hardware, not universal physical grounding."
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    "tags": [
      "robot policies",
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      "source_id": "source-2026-09-22-013",
      "title": "arXiv preprint 2609.24682",
      "publisher": "arXiv",
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      "published_at": "2026-09-21T10:41:08.000-04:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "A Robot Kept the World Model’s Insight, Not Its Bulk",
    "publisher": "The Machine Press",
    "published_at": "2026-09-22T09:00:00.000-04:00",
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