这篇论文对物理AI基准的冗余进行了深入分析，为基准选择提供了新的视角，值得AI领域研究者关注。
构建了51个模型在12个物理AI基准上的矩阵，测量基准之间的信息共享和冗余，发现冗余影响了排名，通过优化选择基准集，保留了78.5%的效用并拟合了Bradley--Terry排名模型。
A Statistical Audit of Physical AI Benchmark Redundancy
Physical AI models are evaluated on suites of benchmarks that differ across model reports, leaving the model-by-benchmark matrix sparse and the relationship between benchmarks unmeasured. We construct a matrix of 51 models on 12 physical AI benchmarks, selected from a registry of 51 benchmarks and 152 models by reporting density, combining scores from model cards and benchmark papers with our own evaluation runs under each benchmark's official protocol. We measure how much information the benchmarks share and show quantitative evidence of Redundancy. Redundancy affects reported rankings: collapsing the two substitute pairs into single columns moves 22 of 51 models by three or more places under an equally weighted average. We then select benchmarks greedily under a utility combining score dispersion with variance not explained by the already-selected set, and obtain a four-benchmark subset retaining 78.5\% of the utility of all 12, on which we fit a Bradley--Terry ranking. The procedure requires only benchmark-level scores with sufficient overlap and is not specific to physical AI.