The Double Measurement Confound in Agent Benchmarks: De-Scaffolding, Ground-Truth Scoring, and Reliability Beyond the Mean
Source
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
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
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.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
Many published AI agentAn 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.Full definition →benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → scores may reflect scaffold design rather than model capability; this gives practitioners a concrete protocol to check whether a leaderboard number actually measures the model they think it does.
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.”
articleEvalDetectBench: A Benchmark for Measuring Evaluation Awareness in Frontier Language ModelsXinning Li, Kemunto Ochwang'i, Aryasomayajula Ram Bharadwaj, Alexandra Souly, Robert Kirk
articleInducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent EvaluationDarragh Quinn, David Dylan, Roisin Healy, Fionn Carroll, Maeve Donnelly, Cormac Sheehan
articleThe Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory AgentsJundong Hu, Shekar Ramachandran