Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?

2026-06-29

AI Summary

This work addresses the lack of systematic evaluation of rubric reliability when using large language models as judges (LLM-as-a-Judge) in complex, long-form agent tasks. The authors propose RuVerBench, the first benchmark specifically designed for rubric validation in agent-centric scenarios, spanning deep research and agent programming domains. Through a large-scale human-annotated dataset, multi-model comparisons, and prompt engineering analyses, they systematically investigate the impact of prompt design, batch processing, and majority voting strategies. Experimental results show that while state-of-the-art models generally perform well, substantial noise remains; weaker models exhibit greater sensitivity to prompt variations, batch processing entails trade-offs between accuracy and efficiency, and majority voting improves reliability but with diminishing returns.

Abstract

Rubric-based scoring has become a widely used paradigm in model evaluation, typically with LLM-as-a-Judge (LaaJ) for rubric scoring. However, the reliability of LaaJ for rubric scoring remains underexplored. This concern is especially pronounced in agentic scenarios, where long, complex outputs further challenge reliable scoring. To address this, we conduct a systematic meta-evaluation of LaaJ reliability for rubric verification. We introduce RuVerBench, the first benchmark for assessing LaaJ reliability in rubric verification for agentic scenarios. RuVerBench covers two prevalent agentic domains, deep research and agentic coding, with 2,458 instances, each containing a model-generated output, a rubric, and a human-annotated label indicating whether the output satisfies the rubric. Using RuVerBench, we evaluate numerous frontier LLMs and find that even the most advanced models achieve strong performance but still exhibit substantial noise. We further analyze the impact of key LaaJ strategies, including prompt design, batching, and majority voting, on rubric verification. We find that weaker models are more sensitive to prompt variations, batched verification presents a trade-off between accuracy and efficiency, and majority voting yields effective but diminishing returns. We have released our dataset and code to facilitate future research: https://github.com/THU-KEG/RuVerBench.

Problem

• LLM-as-a-Judge
• rubric verification
• agentic scenarios
• reliability
• model evaluation

Innovation

• LLM-as-a-Judge
• rubric verification
• agentic scenarios
• RuVerBench
• meta-evaluation