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כתבה arXiv cs.AI ·

The Hitchhikers Guide to Rubric Quality Understanding and Enrichment

תקציר מקורי באנגליתarXiv:2604.01375v3 Announce Type: replace Abstract: Rubrics distill notions of expert quality and measure agent performance. However, the quality of rubrics themselves have not been systematically measured and are often left to downstream performance.We import apparatuses from measurement theory built for exactly this: quantitative signals based on the rubric's content, and introduce the RubrIc-Failure Taxonomy (RIFT), of nine possible ways a rubric fails, organized under reliability and content validity. Every mode leaves a distinct signature. To show the signals track failure causally, we seed 720 corruptions, injecting each RIFT mode into clean rubrics at known severity levels. A linear probe over the signals identifies which mode was injected at $75.0\%$ accuracy, beating $56.7\%$ for
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