REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
📰 ArXiv cs.AI
Learn to attribute errors in LLM agent traces using REFLECT, a method that refines error attribution through intervention outcomes
Action Steps
- Implement REFLECT to identify silent failures in LLM agent traces
- Use intervention outcomes to refine error attribution
- Apply REFLECT to real-world LLM agent tasks to evaluate its effectiveness
- Compare REFLECT with existing error attribution methods
- Configure REFLECT to work with different LLM models and tasks
Who Needs to Know This
ML researchers and engineers working with LLM agents can benefit from this method to improve error detection and attribution in complex task traces
Key Insight
💡 REFLECT refines error attribution through intervention outcomes, improving error detection in LLM agent traces
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🚀 Improve error detection in LLM agents with REFLECT! 🤖
Key Takeaways
Learn to attribute errors in LLM agent traces using REFLECT, a method that refines error attribution through intervention outcomes
Full Article
Title: REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
Abstract:
arXiv:2606.09071v1 Announce Type: new Abstract: 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
Abstract:
arXiv:2606.09071v1 Announce Type: new Abstract: 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
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