arXiv:2606.17541v1 Announce Type: cross 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
The increasing sophistication and autonomy of AI systems necessitates more nuanced and objective evaluation methods beyond simple terminal success metrics.
Improved evaluation techniques for AI agents will accelerate development, lead to more robust systems, and enable better differentiation between competing AI solutions.
The focus shifts from binary success/failure to a more granular understanding of agent performance, considering progress and efficiency throughout a task.
- · AI developers
- · AI research institutions
- · Companies deploying AI agents
- · Developers relying solely on terminal success metrics
More efficient and accurate evaluation of AI agentic systems becomes possible.
This improved evaluation can lead to faster and more effective iterative development cycles for AI agents.
Accelerated development of robust AI agents contributes to their broader adoption and impact on white-collar workflows.
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Read at arXiv cs.AI