EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction
EarlyEval stops agent benchmark runs once intermediate behavior predicts the final result with calibrated confidence.
The paper frames this as a way to cut evaluation cost inside each task, rather than shrinking the benchmark. Its LightGBM success and failure classifiers use behavioral, textual, and reference-solution features with negligible per-step overhead. Across SWE-bench Verified, TerminalBench, and Toolathlon, the authors report eliminating 13%-26% of agent steps, with token reductions up to 44.1% input and 29.4% output. Prediction accuracy is reported at 89%-97%, while average resolve-rate shifts stay around one to two percentage points. Source: HF Daily Papers' note.
The paper frames this as a way to cut evaluation cost inside each task, rather than shrinking the benchmark. Its LightGBM success and failure classifiers use behavioral, textual, and reference-solution features with negligible per-step overhead. Across SWE-bench Verified, TerminalBench, and Toolathlon, the authors report eliminating 13%-26% of agent steps, with token reductions up to 44.1% input and 29.4% output. Prediction accuracy is reported at 89%-97%, while average resolve-rate shifts stay around one to two percentage points. Source: HF Daily Papers' note.
score 5