SOURCE-LINKED INTELLIGENCE
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: colla
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T15:54:15.000Z
First collected: 2026-09-21T09:11:58.312Z. This is not the publication date.