WIRE: Diagnosing Live Within-Policy Instruction Conflicts in LLM Agents
A recent study has unveiled WIRE (Witnessed Intra-policy Rule Evaluation), a system designed to identify conflicts arising from live intra-policy rules in LLM agents that operate under long-term natural-language prompt policies. This research highlights how seemingly reasonable individual rules can unexpectedly interact, leading to conflicts. WIRE extracts rules grounded in sources, encodes them as PyRule clauses, performs satisfiability checks to identify hard-collision candidates, and generates concrete co-governance witnesses to evaluate model outputs against the original source rules. The study analyzed six public prompt policies, extracting 276 source rules and 560 atomic clauses, conducting 30,944 within-policy clause-pair comparisons, retaining 170 hard-collision candidate pairs, and producing 1,402 concrete witnesses. The findings were published on arXiv under ID 2605.27784.
Key facts
- WIRE pipeline diagnoses live intra-policy rule conflicts in LLM agents.
- Conflicts arise from individually reasonable standing rules interacting in uninspected ways.
- WIRE extracts source-grounded rules and encodes them as PyRule clauses.
- Satisfiability checks retain same-surface hard-collision candidates.
- Candidates are realized as concrete co-governance witnesses.
- Model outputs are judged against original source-rule text.
- Across six public prompt policies, 276 source rules and 560 atomic clauses were extracted.
- 30,944 within-policy clause-pair comparisons were classified.
- 170 encoded hard-collision candidate source-rule pairs were retained.
- 1,402 concrete witnesses were realized.
- Published on arXiv with ID 2605.27784.
Entities
Institutions
- arXiv