{
  "id": 14385233,
  "title": "Read the Room Before Your Model Generates",
  "url": "https://urgent.news/2026/10/13/read-the-room-before-your-model-generates",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-13T17:51:18.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.18820v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for selecting temperature typically involve fixed, heuristic global settings and context or token-level calibration, rather than a geometrically interpretable representation of the communicative scenario. In this work, we introduce a simple and…",
  "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."
}