Rubrics have been widely used to evaluate and optimize language models on open-ended tasks.
However, existing work primarily focuses on generating better rubrics, with limited understanding of what makes rubric design effective.
In this work
We conduct a systematic mechanistic analysis of rubric design across criterion organization, criterion operationalization, and score aggregation.
Our analysis shows that structural organization enables more comprehensive criterion construction, semantic specification alone does not ensure effective criterion-level judgments, and sophisticated score aggregation provides limited additional gains.
Guided by these findings
We introduce Adaptive Rubric Tree Search with Hybrid refinement (ARTS-Hybrid), which combines adaptive tree construction with semantic-first, response-calibrated refinement.
Experiments across benchmarks show
That ARTS-Hybrid improves both end-to-end evaluation performance and intrinsic rubric quality.
Further experiments suggest that, beyond evaluation, rubrics generated by ARTS-Hybrid provide effective signals for model optimization.