VERA: Verdict-Conditioned Reliability for Adaptive LLM Judges

Abstract:Accurately estimating judgment reliability is a central challenge in adapting LLM judges to newly verified feedback while preserving previously learned behavior. However, existing approaches often rely on output-level confidence, which can be overconfident and poorly aligned with judgment correctness. We propose VERA, a VErdict-conditioned Reliability Axis that estimates reliability from hidden activations by distinguishing correct from incorrect judgments within each predicted-verdict group. Using VERA as a control signal, we develop a VERA-guided periodic adaptation framework that integrates reliability-ranked corrective updates, reliability-residual replay, and periodic refresh of the reliability directions. After VERA-guided adaptation on Chatbot Arena, 8B- and 14B-parameter judges outperform the strongest baseline on each of four held-out public benchmarks, with relative gains of up to 23.01%. The framework also improves focal-class recall by up to 16.1% relative to the strongest adaptive baselines on a separate proprietary temporal auditing task.

Machine Learning (cs.LG)

arXiv:2610.05452 [cs.LG]

(or arXiv:2610.05452v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.05452 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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