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    "headline": "The Swarm Learns Who Should Speak",
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    "dek": "Market-based post-training lets decentralized agents bid for participation and learn coordination with their individual policies.",
    "summary": "Market-based post-training lets decentralized agents bid for participation and learn coordination with their individual policies.",
    "body_text": "Fixed orchestration can separate what each agent learns from how the group organizes itself. Market-based post-training lets agents bid to participate, uses those bids to determine who acts and converts the collective outcome into local training credit. The authors report that learned self-orchestration supports larger populations at inference time and loses less performance when agents drop out. The paper evaluates controlled multi-agent tasks rather than an open-world organization, so bidding is a training mechanism, not evidence that markets are universally optimal. Its useful move is to make coordination structure itself learnable.",
    "why_it_matters": "Market-based post-training lets decentralized agents bid for participation and learn coordination with their individual policies.",
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      "The paper evaluates controlled multi-agent tasks rather than an open-world organization, so bidding is a training mechanism, not evidence that markets are universally optimal."
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    "tags": [
      "multi-agent systems",
      "orchestration",
      "reinforcement learning"
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    {
      "source_id": "source-2026-10-17-009",
      "title": "arXiv preprint 2610.22322, original v1",
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      "published_at": "2026-10-15T13:51:57.000-04:00",
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    "title": "The Swarm Learns Who Should Speak",
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