REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
Large language model (LLM) agents now solve complex tasks through long plan-and-execution traces, yet the ability to locate errors in a completed traces still lags far behind, especially in the \emph{silent failure} regime. Existing approaches predict suspect steps via classifiers or LLM judges, or recover correct answers via retry, but none feed the intervention outcome back to \emph{refine the attribution itself}. We propose \methodname, a method that closes this gap by diagnosing a candidate error step, testing it through controlled replay with a diagnosis-specific patch, and using the verified outcome flip as contrastive evidence to refine the final attribution. Across four localizati...
| Authors | Xiaofeng Lin, Yingxu Wang, Tung Sum Thomas Kwok et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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