arXiv AI
Executable Prefix Values for Locating Agent Failures
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6d ago
Executable Prefix Values for Locating Agent Failures
The paper investigates whether human or LLM‑judge‑identified ‘first mistake’ points truly indicate failure causes in LLM agents. By reconstructing intermediate states and computing a prefix value V(h_t) for each step, the authors identify a certified point of no return (PNR) and compare it to judge‑labeled steps. Their experiments on 150 SWE‑bench tasks show that judge labels often miss the decisive step, while restarting at the measured PNR improves success rates from 0.44 to 0.57.
arXiv AI
Sep 2
trajectory-judge: What Outcome-Only LLM Judges Miss on Agent Trajectories
The paper "trajectory-judge: What Outcome-Only LLM Judges Miss on Agent Trajectories" examines the limitations of outcome-only evaluation for large language model agents. Using a deterministic tool‑using support‑desk environment with a scripted oracle policy and a fault injector, the authors compare five different judging approaches—programmatic rules, outcome‑only, step‑rubric at two model sizes, and a self‑consistency ensemble—on metrics such as detection, step localisation, fault typing, calibration, and cost across 400 trajectories. The study finds that outcome‑only judges miss many silent faults and generate false positives, while step‑rubric judges achieve higher recall with no false alarms but at greater cost, and that none of the judges read the final reply, allowing fabricated promises to evade detection. "whyItMatters":"The findings highlight that current production‑default outcome‑only evaluations can overlook critical failures in agent behavior, underscoring the need for more nuanced, step‑level judging methods to ensure reliable LLM agent performance."
arXiv AI
Aug 5
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
arXiv:2608. 03222v1 Announce Type: cross Abstract: Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates.
Hugging Face Trending Papers
Aug 4
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion.
arXiv Machine Learning
Jun 10
From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
arXiv:2606. 09863v1 Announce Type: new Abstract: LLM agents can fail silently by asserting task completion when the environment state shows otherwise.
arXiv AI
4d ago
Structural Baselines Matter for Agent Failure Detection and Attribution
arXiv:2610.16661v1 Announce Type: new Abstract: An LLM agent using tools can fail silently. It stops before the task is done, raises no error, and answers fluently. Two kinds of monitor address this,...