Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agents
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Computer Science > Machine Learning
arXiv:2608.29685 (cs)
[Submitted on 30 Aug 2026]
Title:Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agents
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Abstract:Early failure prediction is important for long-horizon agents, as it enables timely intervention and can reduce inference and tool-use costs. Uncertainty quantification, such as verbal confidence and perplexity, offers a promising approach to detecting agent failures; however, it has not been explored whether these signals retain their discriminative power during the intermediate stages of long-horizon execution. We evaluate mainstream uncertainty signals on deep-research tasks and find that verbal confidence reliably distinguishes failures at trajectory completion, achieving a mean AUROC of 0.85, whereas all evaluated signals offer limited predictive value earlier in execution, with none exceeding a mean AUROC of 0.60 at 50% trajectory progress. We identify an underlying mechanism explaining this gap: path switching, where agents frequently abandon their current search direction in-trajectory, breaking the link between early signal and final outcome. These findings challenge the assumption that intermediate uncertainty can reliably guide early intervention. They also motivate a practical recommendation for agent harnesses in deep-research settings: use final-step confidence to decide whether to restart, an approach that our experiments find more effective than in-trajectory intervention.
| Comments: | Accepted to the Main Conference of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.29685 [cs.LG] |
| (or arXiv:2608.29685v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29685
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agents, by Zongyue Li and 5 other authors
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