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  "story": {
    "story_id": "mp-2026-10-24-014",
    "source_story_id": "tmp-story-muffin-agent-memory",
    "edition_id": "mp-2026-10-24-morning-0106",
    "edition_url": "https://themachinepress.com/edition/2026-10-24",
    "position": 14,
    "story_type": "dispatch",
    "section": "frontier-models",
    "editorial_classification": "editorial",
    "headline": "Agents Share Findings Without Sharing Every Thought",
    "slug": "agents-share-findings-without-sharing-every-thought",
    "dek": "MUFFIN uses an asynchronous common buffer for intermediate evidence while private reasoning stays local, reporting a stronger quality-cost trade-off than debate.",
    "summary": "MUFFIN uses an asynchronous common buffer for intermediate evidence while private reasoning stays local, reporting a stronger quality-cost trade-off than debate.",
    "body_text": "Synchronous multi-agent debate can spend many tokens repeating full traces. MUFFIN routes demanding queries to multiple agents that write intermediate findings into a shared memory buffer while keeping private reasoning local. Across twenty benchmark settings, the system achieved the best accuracy in seventeen. Against the cited debate comparison, it improved accuracy by 19.2 points while using 77.4 percent fewer tokens. The gains come from the authors’ routing, prompts, models and benchmarks, and do not show that more agents help every task.",
    "why_it_matters": "MUFFIN uses an asynchronous common buffer for intermediate evidence while private reasoning stays local, reporting a stronger quality-cost trade-off than debate.",
    "limitations": [
      "The gains come from the authors’ routing, prompts, models and benchmarks, and do not show that more agents help every task."
    ],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-10-24-014/agents-share-findings-without-sharing-every-thought",
    "json_url": "https://themachinepress.com/story/mp-2026-10-24-014.json",
    "first_published_at": "2026-10-24T09:00:00.000-04:00",
    "modified_at": "2026-10-24T09:00:00.000-04:00",
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        "text": "MUFFIN uses an asynchronous common buffer for intermediate evidence while private reasoning stays local, reporting a stronger quality-cost trade-off than debate.",
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          "source-2026-10-24-014"
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        "qualification": "The gains come from the authors’ routing, prompts, models and benchmarks, and do not show that more agents help every task."
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    "source_ids": [
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    "tags": [
      "multi-agent systems",
      "memory",
      "efficiency"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-10-24-014",
      "title": "arXiv preprint 2610.31355, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.31355v1",
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      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-10-22T10:36:46.000-04:00",
      "accessed_at": "2026-10-24T08:23:48.747-04:00",
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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": "Agents Share Findings Without Sharing Every Thought",
    "publisher": "The Machine Press",
    "published_at": "2026-10-24T09:00:00.000-04:00",
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