PACE: A Proxy for Agentic Capability Evaluation
Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic LLM benchmarks that test individual capabilities (e.g., reasoning, code generation) are fast and cheap to run. In this paper, we investigate whether performance on expensive agentic benchmarks can be accurately predicted by the performance on a small, carefully selected subset of atomic evaluation instances. We introduce PACE, a framework that constructs proxy benchmarks by selecting instances from existing non-agentic evaluations whose aggregate scores most r...
| Authors | Yueqi Song, Lintang Sutawika, Jiarui Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 6 July 2026 |
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