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Presents Oat, a lightweight unsupervised method for identifying error steps in LLM-based agentic failure trajectories using neural controlled differential equations trained only on successful trajectories. It achieves 200-5000x speedup over prompting baselines with significant F1 improvements in in-domain and out-of-distribution settings.