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  "story": {
    "story_id": "mp-2026-09-19-001",
    "source_story_id": "tmp-lead-smelldiffusion-olfactory-navigation",
    "edition_id": "mp-2026-09-19-morning-0072",
    "edition_url": "https://themachinepress.com/edition/2026-09-19",
    "position": 1,
    "story_type": "lead",
    "section": "robotics",
    "editorial_classification": "editorial",
    "headline": "The Robot Followed a Smell Through the Wind",
    "slug": "the-robot-followed-a-smell-through-the-wind",
    "dek": "A geometric gate cut displaced source error by 60%, while one diffusion proposal took 41.7 milliseconds.",
    "summary": "A geometric gate cut displaced source error by 60%, while one diffusion proposal took 41.7 milliseconds.",
    "body_text": "SmellDiffusion represents species-specific gas zones in an open-vocabulary scene graph, estimates a named leak's source, and gives the same goal to classical and diffusion planners. In 424 solved source-wind configurations, 28 concentration peaks sat more than half a meter from the true source. A gate trained only on the training split detected nine of ten held-out displacements at 0.64 precision and reduced mean error on displaced cases from 1.468 meters to 0.592 meters. One diffusion proposal ran faster than gas-guided A*, but best-of-ten sequential sampling cost more overall; plain A* remained the fastest shortest-path method.",
    "why_it_matters": "A geometric gate cut displaced source error by 60%, while one diffusion proposal took 41.7 milliseconds.",
    "limitations": [
      "A gate trained only on the training split detected nine of ten held-out displacements at 0.64 precision and reduced mean error on displaced cases from 1.468 meters to 0.592 meters."
    ],
    "importance": 10,
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    "first_published_at": "2026-09-19T09:00:00.000-04:00",
    "modified_at": "2026-09-19T09:00:00.000-04:00",
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        "text": "A geometric gate cut displaced source error by 60%, while one diffusion proposal took 41.7 milliseconds.",
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        "qualification": "A gate trained only on the training split detected nine of ten held-out displacements at 0.64 precision and reduced mean error on displaced cases from 1.468 meters to 0.592 meters."
      }
    ],
    "source_ids": [
      "source-2026-09-19-001"
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    "tags": [
      "machine olfaction",
      "quadruped navigation",
      "gas-source localization"
    ],
    "image_url": "https://themachinepress.com/issues/2026-09-19/lead-smelldiffusion.png",
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-09-19-001",
      "title": "arXiv preprint 2609.20624",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.20624",
      "canonical_url": "https://arxiv.org/abs/2609.20624",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-09-17T12:09:04.000-04:00",
      "accessed_at": "2026-09-19T08:30:21.326-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
  },
  "cite_this_report": {
    "title": "The Robot Followed a Smell Through the Wind",
    "publisher": "The Machine Press",
    "published_at": "2026-09-19T09:00:00.000-04:00",
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