When to Intervene? An Empirical Study of Errors and Self-Recovery in LLM Agents

Authors: Chiming Duan , Tong Jia , Yingxi Chen , Minghua He , Lingzhe Zhang , Ying Li

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Abstract: LLM agents interact with Web services to perform tasks on users' behalf. Errors during these interactions can disrupt execution and prevent task completion, motivating external intervention. However, agents may self-recover through continued execution, raising the question of when to intervene and when to wait for self-recovery. We present an empirical study of errors and self-recovery in LLM agents. We construct ReInBench, a dataset containing 1,259 agent traces and 1,403 error events across computer-use and Web tasks. Our study examines how often and how quickly agents self-recover, how self-recovery relates to subsequent execution and task completion, and whether self-recovery can be predicted when an error occurs. We find that 725 error events self-recover, often within a few steps, with self-recovery performance differing across Execution LLMs. Self-recovery can also help agents recover from subsequent errors of the same type and support overall task completion. Intervention should therefore prioritize errors unlikely to self-recover, while allowing agents to continue when self-recovery is likely. Building on these empirical findings, we propose ReCoF, a method that predicts whether an error will self-recover to inform whether additional intervention is needed. ReCoF achieves an average relative improvement of 9.02% in Macro-F1 over the strongest baseline on ReInBench. Our code and data can be found at this https URL .

Comments:

11 pages, 8 figures, 2 tables

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.36471 [cs.AI]

(or

arXiv:2610.36471v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36471

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chiming Duan [ view email ]

[v1]

Tue, 27 Oct 2026 01:26:53 UTC (4,063 KB)

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