{
  "id": 17176443,
  "title": "Learning Calibrated Confidence for Multi-Agent Collaboration",
  "url": "https://urgent.news/2026/10/29/learning-calibrated-confidence-for-multi-agent-collaboration",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-29T12:30:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.39927v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering. Multi-agent systems address such tasks by dividing work among specialized agents, where upstream agents produce intermediate answers and supporting evidence that downstream agents build upon. However, upstream answers vary in correctness,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}