Auditing Action Settlement in LLM Agent Environments
A study audits five settlement policies for concurrent actions in LLM agent environments, focusing on order sensitivity, useful progress, and replay consistency.
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Evidence
- AarXiv cs.AIPrimary source2026-10-02 12:00
Five settlement policies are tested in 28,800 exhaustive permutation trials and 2,160 scripted multistep episodes.
- AarXiv cs.AIPrimary source2026-10-02 12:00
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 ep…