{
  "id": 6088836,
  "title": "GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity",
  "url": "https://urgent.news/2026/09/04/gut-quantifying-and-optimizing-the-reasoning-uncertainty-of-llms-via",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-04T15:40:12.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.05284v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs often produce a proliferation of divergent branches at each reasoning step even when fed the same prompting inputs, and certain branches exhibit evidently incredible, even nonsensical, reasoning chains and results. In…",
  "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."
}