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  "story": {
    "story_id": "mp-2026-09-25-011",
    "source_story_id": "tmp-story-pairwise-multirobot-plan-regret",
    "edition_id": "mp-2026-09-25-morning-0078",
    "edition_url": "https://themachinepress.com/edition/2026-09-25",
    "position": 11,
    "story_type": "dispatch",
    "section": "robotics",
    "editorial_classification": "editorial",
    "headline": "Pairwise Scoring Picked the Wrong Robot Plan on Six of Seven Maps",
    "slug": "pairwise-scoring-picked-the-wrong-robot-plan-on-six-of-seven-maps",
    "dek": "Dropping three- and four-robot interactions produced regret as high as 0.337 of map coverage.",
    "summary": "Dropping three- and four-robot interactions produced regret as high as 0.337 of map coverage.",
    "body_text": "Researchers replayed all 16 subsets of each four-robot exploration plan to calculate exact delivered coverage, then compared that ranking with scores built only from singleton and pair terms. At the 15-meter candidate range, the order-two approximation changed the selected plan on six of seven maps in each of two plan families, with regret up to 0.337 of total map coverage. A least-squares pairwise fit improved the result but still changed the winner on three of seven maps per family.",
    "why_it_matters": "Dropping three- and four-robot interactions produced regret as high as 0.337 of map coverage.",
    "limitations": [
      "Researchers replayed all 16 subsets of each four-robot exploration plan to calculate exact delivered coverage, then compared that ranking with scores built only from singleton and pair terms."
    ],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-09-25-011/pairwise-scoring-picked-the-wrong-robot-plan-on-six-of-seven-maps",
    "json_url": "https://themachinepress.com/story/mp-2026-09-25-011.json",
    "first_published_at": "2026-09-25T09:00:00.000-04:00",
    "modified_at": "2026-09-25T09:00:00.000-04:00",
    "content_status": "new",
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    "carryover_reason": null,
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        "text": "Dropping three- and four-robot interactions produced regret as high as 0.337 of map coverage.",
        "source_ids": [
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        "qualification": "Researchers replayed all 16 subsets of each four-robot exploration plan to calculate exact delivered coverage, then compared that ranking with scores built only from singleton and pair terms."
      }
    ],
    "source_ids": [
      "source-2026-09-25-011"
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    "tags": [
      "multi-robot planning",
      "coverage",
      "approximation"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-09-25-011",
      "title": "arXiv preprint 2609.29929",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.29929",
      "canonical_url": "https://arxiv.org/abs/2609.29929",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-09-24T10:59:20.000-04:00",
      "accessed_at": "2026-09-25T08:29:03.434-04:00",
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  "corrections": [],
  "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": "Pairwise Scoring Picked the Wrong Robot Plan on Six of Seven Maps",
    "publisher": "The Machine Press",
    "published_at": "2026-09-25T09:00:00.000-04:00",
    "canonical_url": "https://themachinepress.com/story/mp-2026-09-25-011/pairwise-scoring-picked-the-wrong-robot-plan-on-six-of-seven-maps"
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