OAT

OAT: Unsupervised failure attribution for LLM agents via one-class learning on successful trajectories

A new arXiv preprint introduces OAT, a method for attributing failures in LLM-based agentic systems by training exclusively on successful trajectories using one-class learning with neural controlled differential equations. At inference time, each step in a failure trajectory receives an anomaly score based on deviation from learned success dynamics. OAT is reported to be 200–5000× faster than prompting-based baselines while outperforming them by +20% F1 in-domain and +7% out-of-distribution, using only 100 successful trajectories for training. The approach addresses a practical bottleneck in agentic system debugging by eliminating the need for costly step-level failure annotations.