Benchmark Score Redundancy

Zeng & Papailiopoulos: an 84-model × 133-benchmark public score matrix is effectively rank-2, so BenchPress matrix completion predicts held-out scores to ~4.6 MedAE and a 5-benchmark probe set recovers a full scorecard. DeepMind's CollabEval takes the same premise down to models × prompts and inverts its use — completion output becomes a control variate inside prediction-powered inference, so the redundancy buys unbiased estimates with valid confidence intervals whose correctness survives the matrix not being low-rank at all (and item-level matrices need ~16 components, not 2).