{
  "id": 11077219,
  "title": "BELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence",
  "url": "https://urgent.news/2026/09/30/beliefrag-making-adaptive-rag-state-aware-under-evolving-evidence",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-30T07:08:27.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.39139v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Adaptive RAG uses signals such as confidence, relevance, support, and retrieval quality to decide when to search or correct evidence. In multi-step retrieval, however, these local signals must be combined into a persistent view of what the current evidence supports, what remains missing, and which action should follow. Existing methods often use such signals as separate triggers, making it…",
  "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."
}