Published on July 6, 2026, the post presents PACE, which uses regression on a small set of non-agentic tasks to estimate agents’ full performance.
Published on July 6, 2026, the post describes PACE, a framework that applies regression to a small set of non-agentic tasks to predict agents’ full performance. According to the post, PACE-BENCH achieves mean absolute error below 4%, about 85% ranking accuracy, and roughly 100 times lower cost.
The post says a cheaper proxy for agent benchmarks could help teams evaluate model changes with less time and computing. To verify the figures and understand the method, consult the original publication and check its metrics, tasks, and comparison conditions; those details are not included in the available summary.