arXiv AI
Auditing Failure Attribution: Label Predictability and Judge Behavior
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arXiv AI
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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."
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Conditional Accuracy Profiles: Diagnosing LLM Judges across Deployment Conditions
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arXiv AI
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Structural Baselines Matter for Agent Failure Detection and Attribution
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arXiv AI
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Who Drifted: the System or the Judge? Anytime-Valid Attribution in LLM Evaluation Pipelines
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arXiv AI
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Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
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arXiv Computation and Language
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TRACE: Diagnosing Verifier Brittleness in Agentic Evaluation
The paper introduces TRACE, a protocol designed to diagnose whether changes in verifier scores for large language model agents reflect actual changes in agent behavior or merely alterations in the evaluation process. TRACE works by applying targeted changes to evaluation components, running paired experiments, and re‑scoring unchanged trajectories to isolate the source of score variation. Experiments on synthetic tasks and public benchmark tasks demonstrate that seemingly significant score shifts can often be attributed to evaluation artifacts, while TRACE can also detect genuine behavioral changes.