{
  "id": 250142,
  "title": "Learning When to Trust via Selective Context Preference Optimization",
  "url": "https://urgent.news/2026/08/06/learning-when-to-trust-via-selective-context-preference-optimization",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-06T17:59:58.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.06377v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Language models increasingly condition their answers on external signals, and a single misleading one can turn a correct answer wrong. The obvious remedy, training models to resist such signals, hides a failure mode: a model that ignores all context looks robust yet is useless when the context is worth trusting. We recast the problem as selective trust and introduce MIST, a human-annotated…",
  "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."
}