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Balance of Benchmarks: Semantic Density Reweighting for Task-Conditioned Model Comparison

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Model comparison increasingly relies on large collections of publicly reported benchmark scores, yet common aggregation strategies trade off evidence coverage against control over capability weighting. Manually curated suites leave potentially informative evaluations unused, while uniform averaging retains them but gives greater influence to capabilities that happen to be benchmarked more densely. We introduce Balance of Benchmarks (BoB), a framework that retains eligible benchmark evidence while adapting its influence for task-conditioned model comparison using only public aggregate scores. B

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