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arXiv Machine Learning By Do Nguyen-Thanh, Junlin Fang, Bing Li, Zhen Fang, Sean Du

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

Read the original on arXiv Machine Learning →

The paper introduces SOAP, a method for attributing failures in large language model agents without requiring step-level labels or a separate set of successful trajectories. SOAP builds a spectral reference space from LLM representations to assign base error scores to each step, then uses attention-based soft dependencies to propagate downstream error signals back to their likely upstream causes. Experiments demonstrate that SOAP outperforms existing baselines in accurately identifying decisive steps responsible for task failures.

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 Machine Learning.

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