aiqbench analysis
This research addresses the statistical inefficiency in evaluating agentic systems by shifting from binary success metrics to preference-based trajectory analysis, which captures partial progress and significantly reduces comparison ties. By improving discriminative power and ranking stability across diverse benchmarks, this method offers a more robust framework for assessing complex AI agents without relying solely on terminal outcomes.
Offline Preference-Based Trajectory Evaluation
arXiv:2606.17541v1 Announce Type: new Abstract: Offline evaluation of agentic systems often collapses trajectories to terminal success, discarding information about partial progress and inducing widespread ties, creating substantial statistical inefficiency by reducing effective sample size and weakening the ability to distinguish systems. We propose preference-based trajectory evaluation, which compares trajectories directly through temporal preferences over progress and time-to-return profiles. We find that, across diverse agentic and interactive benchmarks, standard success-based metrics produce tied comparisons on roughly 75% of instances, whereas trajectory-aware preferences reduce ties to roughly 35%, improving discriminative power, ranking stability, and data efficiency. Our results suggest that benchmark saturation, often attributed to poor data collection or problem difficulty, may also be explained by the choice of evaluation measure.
Abstract:Offline evaluation of agentic systems often collapses trajectories to terminal success, discarding information about partial progress and inducing widespread ties, creating substantial statistical inefficiency by reducing effective sample size and weakening the ability to distinguish systems. We propose preference-based trajectory evaluation, which compares trajectories directly through temporal preferences over progress and time-to-return profiles. We find that, across diverse agentic and interactive benchmarks, standard success-based metrics produce tied comparisons on roughly 75% of instances, whereas trajectory-aware preferences reduce ties to roughly 35%, improving discriminative power, ranking stability, and data efficiency. Our results suggest that benchmark saturation, often attributed to poor data collection or problem difficulty, may also be explained by the choice of evaluation measure.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.17541 [cs.LG]
(or arXiv:2606.17541v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17541 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 211 words