PACE: A Proxy for Agentic Capability Evaluation

2026-07-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

PACE is a novel framework designed to efficiently evaluate large language model (LLM) agentic capabilities by predicting performance on expensive benchmarks like SWE-Bench and GAIA. Traditional agentic evaluations can cost thousands of dollars and take days. PACE addresses this by constructing proxy benchmarks, termed PACE-Bench, which select a compact subset of instances from existing non-agentic evaluations. It uses a regression model to map scores on these atomic instances to a model's performance on target agentic benchmarks. The instance selection combines target-relevance local selection and globally informative global selection strategies. Across experiments involving 14 models, 4 agentic benchmarks, and 19 non-agentic benchmarks, PACE-Bench achieved a leave-one-out cross-validation (LOOCV) mean absolute error (MAE) under 4%, Spearman correlation above 0.80, and pairwise model-ranking accuracy around 85%, all while incurring less than 1% of the full evaluation cost.

Key takeaway

For MLOps Engineers and AI Scientists developing or selecting LLM agents, you can now obtain reliable performance estimates without the prohibitive cost and time of full agentic evaluations. PACE-Bench offers a practical solution, predicting agentic scores with high accuracy (MAE <4%, correlation >0.80) at less than 1% of the traditional evaluation cost. Integrate this proxy benchmark into your development pipeline to accelerate iteration, optimize model selection, and make informed routing decisions efficiently.

Key insights

Agentic LLM performance can be accurately predicted from a compact, curated set of non-agentic evaluation instances at minimal cost.

Principles

• Agentic performance is reliably proxied by atomic capabilities.
• Strategic instance selection enhances predictive accuracy.
• Significant cost reduction in LLM evaluation is possible.

Method

PACE constructs proxy benchmarks by selecting non-agentic instances via local and global strategies, then fits a regression to predict agentic scores.

In practice

• Estimate agentic performance during model development.
• Inform model selection and routing decisions.
• Identify unique skill demands of agentic benchmarks.

Topics

• LLM Agents
• Agentic Capability Evaluation
• Proxy Benchmarks
• Model Performance Prediction
• Evaluation Cost Reduction

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.