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arXiv AI By Chiming Duan, Tong Jia, Yingxi Chen, Minghua He, Lingzhe Zhang, Ying Li

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

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The paper studies when to intervene in large language model (LLM) agents that interact with web services. Using the ReInBench dataset of 1,259 agent traces and 1,403 error events, the authors find that 725 errors self‑recover, often within a few steps, and that self‑recovery can aid subsequent error handling and overall task completion. They propose ReCoF, a predictor that decides whether an error will self‑recover, achieving a 9.02% relative improvement in Macro‑F1 over the best baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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