RADAR: Rubric-Aware Dependency and Redundancy Analysis for LLM-as-Judge Evaluation

• URL: http://arxiv.org/abs/2608.01810v1
• Date: Mon, 03 Aug 2026 07:21:32 GMT
• Title: RADAR: Rubric-Aware Dependency and Redundancy Analysis for LLM-as-Judge Evaluation
• Abstract summary: We introduce RADAR, a diagnostic framework for estimating behaviorally coupled evaluation criteria.<n>Given a rubric, RADAR generates targeted synthetic probes, scores each probe on all criteria, and produces a directional coupling matrix.<n>Using only a small number of probes per criterion, RADAR recovers human inter-criterion correlation structure.
• Score: 3.339065092252818
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rubric-based LLM-as-judge pipelines often assume that evaluation criteria provide independent signals. In practice, however, criteria can be behaviorally coupled: improving one criterion may systematically change scores on another, distorting aggregate scores used in model-release or product-update decisions. We introduce RADAR, a lightweight preflight diagnostic framework for estimating such coupling before large-scale evaluation. Given a rubric, RADAR generates targeted synthetic probes, scores each probe on all criteria, and produces a directional coupling matrix that shows which criteria co-score and how. We validate RADAR on three industry-relevant evaluation settings: NVIDIA HelpSteer2, SumPubMed, and the Yale-Salesforce SummEval benchmark. Using only a small number of probes per criterion, RADAR recovers human inter-criterion correlation structure (Pearson r > 0.84) and provides practitioners with concrete audit signals about redundancy, hierarchy, and aggregation sensitivity before committing to large-scale judging.

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