Diagnosing Live Within-Policy Instruction Conflicts in LLM Agents with Witnessed Resolution Profiles
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
Record details
Published: 27 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Healthcare · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (27 May 2026), “Diagnosing Live Within-Policy Instruction Conflicts in LLM Agents with Witnessed Resolution Profiles,” evidence record 3638, https://ethics.ai/record/3638 (originally published by arXiv).
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