{
  "id": 3638,
  "url": "https://arxiv.org/abs/2605.27784v1",
  "title": "Diagnosing Live Within-Policy Instruction Conflicts in LLM Agents with Witnessed Resolution Profiles",
  "summary": "LLM agents are governed by long-lived natural-language prompt policies, but individually reasonable standing rules can interact in uninspected ways. We study live intra-policy rule-conflict diagnosis: finding rule pairs inside a single prompt policy that can co-govern a realistic state, and measuring how models resolve that pressure in responses or tool actions. We introduce WIRE, a Witnessed Intra-policy Rule Evaluation pipeline. WIRE extracts source-grounded rules, encodes them as PyRule claus",
  "authors": "Lu Yan, Xuan Chen, Xiangyu Zhang",
  "category": "research",
  "topics": "regulation,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T00:09:45.000Z",
  "fetched_at": "2026-07-14T16:30:23.247Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3638",
  "original_url": "https://arxiv.org/abs/2605.27784v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}