Intel / article

The Double Measurement Confound in Agent Benchmarks: De-Scaffolding, Ground-Truth Scoring, and Reliability Beyond the Mean

Source: arxiv.org
Author: Yonghong Zhang, Shadi Motaali, Vu Phong Dinh, Avin Piroutiniya, Jorge E. L\'opez de Vergara, Luis de Pedro, Ricardo Correia, Isabel M. Parra, Yong Xie
Date: Published Sep 10, 2026

Context

An leaderboard can obscure model differences if surrounding software makes important decisions for the model and the scorer rewards output form rather than correctness. This paper examines those two problems together. Its proposed transfers execution decisions to the model, scores answers against known correct results and examines poor runs as well as average performance.

The authors report that applying these changes to ComtradeBench separated models that had previously appeared closely matched, revealing differences in performance and consistency. The implication for comparing agents is that a score needs an account of what the model actually decided and what the evaluator counted as success.

Terms in this piece · Glossary

AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.

Key quotes

“We identify a double measurement confound: execution-critical decisions are performed by a fixed scaffold instead of the model, while the scorer evaluates outputs using criteria that may not reflect task correctness.”

“Experiments on ComtradeBench show that the joint intervention transforms a nearly flat leaderboard into a reliability spectrum that distinguishes both average performance and robustness across seeds.”

Read the source arxiv.org