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Research questionHow can we reduce per-task LLM-agent evaluation cost without distorting benchmark outcomes?A single LLM-agent benchmark run can be expensive, and iterative development repeats that cost. Reducing the number of tasks does not reduce the execution cost of each task that remains.
AI
AI Agents
Evaluation & Benchmarks
Inference Optimization
Latest papersRecent research connected to this question, newest first.EarlyEval: Cheaper Agent Evaluation via Early Outcome PredictionThe source studies early outcome prediction for LLM agents on SWE-bench Verified, TerminalBench, and Toolathlon. Its framework uses intermediate behavioral, textual, and reference-solution features to halt runs early; reported results include 13%–26% fewer steps, up to 44.1% fewer input tokens, and 29.4% fewer output tokens, with 89%–97% prediction accuracy and average resolve-rate changes of one to two percentage points.research paper · Sep 2, 2026
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