@ms_aifrontiers: Most LLM benchmark scores are predictable before you ever run them. New from the MS AI Frontiers team: BenchPress. The …
Summary
The MS AI Frontiers team introduces BenchPress, a method that uses matrix completion to predict LLM benchmark scores from just five probes, showing the score matrix is effectively rank-2.
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Cached at: 06/25/26, 09:28 PM
Most LLM benchmark scores are predictable before you ever run them. New from the MS AI Frontiers team: BenchPress. The 84-model × 133-benchmark score matrix turns out to be effectively rank-2, so matrix completion fills in the rest. 5 probes recover a model’s whole profile. https://t.co/uxyGfzmrt9
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This research paper demonstrates that the scores of frontier AI models across 133 benchmarks are approximately rank-2, meaning only two latent factors explain over 90% of variation. The authors introduce BenchPress, a logit-space matrix completion method that predicts a model's full scorecard from just a few benchmarks, significantly reducing the cost of evaluation.
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