The PACE paper submitted to arXiv on July 2, 2026 proposes using compact proxy benchmarks to estimate expensive agentic benchmark scores before teams run full evaluations. The authors report tests across 14 models, four agentic benchmarks, and 19 non-agentic benchmark pools, with PACE-Bench predicting agentic scores at under 4% leave-one-out mean absolute error, above 0.80 Spearman correlation, and around 85% pairwise ranking accuracy. The reported cost is less than 1% of a full agentic evaluation. For practitioners, the useful signal is triage: proxy evals can help narrow model, routing, or tool-policy candidates, but the paper does not prove that compact subsets can replace production-grade agent tests on reliability, tool failures, or long-horizon behavior.
