From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
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Computer Science > Machine Learning
Title:From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
Abstract:LLM agents can fail silently by asserting task completion when the environment state shows otherwise. We study this failure mode, false success, across two agent benchmarks: 9,876 tau2-bench trajectories from 8 model families and 1,879 AppWorld trajectories from 4 model families with text-independent ground truth. False success is common but varies by setting: 45--48% of failures in single-control tau2-bench domains, 3% in dual-control telecom, and 75.8% among AppWorld self-assessing coding-agent trajectories with explicit status claims. LLM judges fail reliably: no configuration across 5 judges, 5 prompt strategies, and full task specifications exceeds AUROC 0.65 on tau2-bench, and the same judges reach only 0.54 AUROC on AppWorld API-call traces. Judges rely on surface completion proxies -- confident closing language in tau2-bench and coarse action-sequence volume in AppWorld -- rather than verified state changes. Lightweight TF-IDF detectors achieve task-disjoint AUROC 0.83 on tau2-bench and 0.95 on AppWorld, recovering 4--8x more false successes than the best judge at the same flag rate with 3,300x lower latency. These results suggest that production monitoring should use lightweight, domain-calibrated detectors as triage signals rather than relying on LLM judges as the primary monitor for false success.
| Comments: | Accepted to FAGEN@ICML2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.09863 [cs.LG] |
| (or arXiv:2606.09863v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.09863
arXiv-issued DOI via DataCite
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