Benchmark compression is a technique that cuts the cost of evaluating AI models by selecting a small, representative set of samples from a larger benchmark. The goal is to keep the compressed benchmark informative enough to approximate full evaluation results, while saving time and compute.
Existing compression methods usually guide sample selection using a single metric, such as overall accuracy. But benchmarks often assess models along multiple dimensions—for example, robustness, fairness, or task-specific performance—and a single-metric approach may miss important trade-offs.
HoloCosm, described in a new arXiv paper, aims to go beyond single-metric compression by selecting samples based on multiple metrics simultaneously. The abstract states that the method addresses this gap, though the full details are not included in the available text.
The paper is listed as arXiv:2610.26714v1. Because the abstract is truncated, specific results or comparisons with existing methods are not yet available from this source alone.