Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay
A typed settlement contract audits concurrent LLM agent actions, showing joint policies complete 59% more six-agent doorway tasks than conservative rejection, with exact replay of 156 checkpoints and rejection of 1,332 corruptions.
Published Oct 1, 2026arXiv ↗
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The audit delivers rigorous replay consistency across 156 checkpoints and 1,332 corruptions with a striking 59-point settlement gap, though "useful progress" and order-sensitive settlement semantics obscure fairness limits and policy novelty.
Abstract
Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a typed snapshot-settlement contract and audit three distinct properties: order sensitivity, useful progress, and replay consistency. Five settlement policies are tested in 28,800 exhaustive permutation trials and 2,160 scripted multistep episodes. Joint policies are spatially order-invariant conditional on fixed priorities, yet conservative rejection completes only 31.25% of agents in a six-agent doorway task versus 90.28% for random tickets; the paired improvement is 59.03 percentage points (95% bootstrap interval: 50.00-68.06). All policies preserve the tested spatial constraints, and priority arbitration still misses the independent small-instance optimum. A separate full-state journal audit exactly replays 156 checkpoints and rejects 1,332 constructed corruptions with a retained terminal anchor. The evidence concerns execution semantics, not human realism or long-run fairness.