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HoloCosm: Beyond Single-Metric Benchmark Compression Across Multiple Metrics

Yupei Lin, Zhongzhan Huang, Junxin Li, Guoming Ling, Shanshan Zhong, Hefeng Wu, Yukai Shi, Liang Lin · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26714 · cited by 0 works (OpenAlex) · open-access PDF

German

Benchmark-Kompression reduziert die Bewertungskosten durch die Auswahl einer kleinen Reihe von Proben. Bestehende Methoden führen typischerweise die Auswahl mit einer einzelnen Metrik, obwohl Benchmarks häufig Modelle entlang mehrerer Dimensionen beurteilen. Wir finden, dass die Modellranglinien oft schwach über Metriken korreliert sind, was darauf hindeutet, dass verschiedene Metriken unterschiedliche Aspekte der Modellleistung in über 70\% der cross-metric Vergleiche erfassen. Zur Bewältigung dieser Herausforderung bewahren die Proben für eine einzelne Metrik nicht zuverlässig Punkte und Ranglisten auf andere, wenn sie für komprimierte Bewertung verwendet werden. Bei 80\% Kompression minimiert jeder der fünf einzelnen Metrik-Baseline einen höheren Rekonstruktionsfehler als

Machine translation (Meta M2M100); the English original follows.

Abstract

Benchmark compression reduces evaluation cost by selecting a small set of samples. Existing methods typically guide selection using a single metric, although benchmarks often assess models along multiple dimensions. We find that model rankings are often weakly correlated across metrics, indicating that different metrics capture distinct aspects of model performance. Samples selected for one metric do not reliably preserve scores and rankings on others when used for compressed evaluation. At 80\% compression, each of the five single-metric baselines yields higher reconstruction error than equal-size random sampling in over 70\% of cross-metric comparisons. To address this challenge, we present HoloCosm, which jointly minimizes normalized score-reconstruction error across source models and metrics while constraining ranking distortion. We evaluate HoloCosm across multiple compression rates on 15 language, multimodal, VLA, and agent benchmark configurations with 85 component metrics. With 80\% compression, HoloCosm outperforms Random, the strongest baseline overall: average NMAE drops by 35.3\% to 0.236, worst-component NMAE drops by 39.5\% to 0.330, and average Spearman correlation rises from 0.884 to 0.915. In addition, HoloCosm remains competitive with existing baselines in single-metric evaluation. We will release our code and experimental results.

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Topics: Benchmark (surveying), Metric (unit), Computer science, Compression (physics), Set (abstract data type), Data mining

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