Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay
On the map: LLM agents and tool use → Software engineering coding agents also fits: General LLM agent frameworks and benchmarks
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 12 references that are in the map. First posted on arXiv 1 Oct 2026.
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10 of 12 references.
References · 10
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- 2026Opinion Consensus Formation Among Networked Large Language Modelscited by 5
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- 2026AgentAbstain: Do LLM Agents Know When Not to Act?