{
  "id": 9463049,
  "title": "When Context Misleads: In-context Learning with Jurisdiction in Large Language Models",
  "url": "https://urgent.news/2026/09/23/when-context-misleads-in-context-learning-with-jurisdiction-in-large",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T09:21:14.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.27603v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: they excel at extracting patterns from demonstrations while often neglecting context authority, the ability to determine whether contextual information should govern the final answer. To benchmark this capability, we introduce FakeContextBench, which…",
  "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."
}