WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents
LLM agents are governed by long-lived prompt policies, where individually reasonable stand- ing rules can jointly govern the same pre- generation state. Existing instruction-following evaluations usually ask whether a model satis- fies explicit constraints, but they do not show how a model resolves pressure among rules inside one standing policy. We introduce WIRE, a witnessed resolu- tion profiler for prompt policies. WIRE ex- tracts source-grounded rules, encodes them as PYRULE clauses, uses satisfiability checks only to nominate same-surface hard-collision can- didates, realizes those candidates as concrete co-governance witnesses, and executes subject models to produce a four-cell res...
| Authors | Lu Yan, Xuan Chen, Xiangyu Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 26 July 2026 |
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