Diagnosing Live Within-Policy Instruction Conflicts in LLM Agents with Witnessed Resolution Profiles
arXiv:2605.27784v1 Announce Type: new Abstract: 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 clauses, uses satisfiability checks to retain same-surfac
As LLM agents become increasingly complex and are deployed in real-world scenarios, the critical need for reliable conflict resolution and debugging tools surfaces.
A strategic reader should care because resolving intra-policy conflicts is crucial for the safe, predictable, and effective operation of autonomous AI agents, impacting their commercial viability and public trust.
The introduction of WIRE provides a systematic method for diagnosing and understanding how LLM agents resolve conflicting instructions within their prompt policies, moving beyond ad-hoc debugging.
- · LLM agent developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · Developers relying on opaque or unpredictable agent behaviors
- · Systems lacking robust conflict resolution mechanisms
Improved reliability and predictability of LLM agent behavior.
Faster development and deployment cycles for complex autonomous AI systems, leading to broader adoption.
Enhanced trust in AI agents could accelerate their integration into critical infrastructure and decision-making processes.
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Read at arXiv cs.AI