Newest papers

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

Japanese

ベンチマーク圧縮単一は、サンプルの小さなセットを選択することによって評価コストを削減します。既存の方法は、単一メトリックを使用して選択を指示しますが、ベンチマークはしばしば複数の次元に沿ってモデルを評価します。我々は、モデルランキングはしばしばメトリック全体に弱く関連し、異なるメトリックがモデルパフォーマンスの70%以上で異なる側面をキャプチャすることを示しています。一つのメトリックに選択されたサンプルは、圧縮評価のために使用される場合に、信頼性の高いスコアとランキングを維持しません。80%圧縮では、5つの単一メトリックベースラインのそれぞれが、ランキング歪みを制限しながら

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange