PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents
Score
77首次系统评估智能体经验保留与自我改进的因果关系，填补了该方向的评测空白。
实验设计严谨，覆盖多模型多框架，并引入路径证据分析，结论可信度高。
开源基准和改进方法可直接用于个人智能体开发者评测与优化，实用性突出。
个人智能体自我改进是当前Agent研究的活跃前沿，该基准正切中热点。
来自arXiv预印本，尚未正式同行评审，但代码已公开，透明性较好。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
A rigorous arXiv paper with broad model/framework coverage and open-sourced benchmark and method, offering strong practical utility.
AI commentary
「What stands out is that the paper goes beyond headline numbers and uses pathway evidence to expose superficial improvements. For agent product builders, Hermes+'s five interventions are highly concrete, especially the outdated-state replacement step, which directly targets a common pain point. If PAST-Bench gains community adoption, it could become the standard litmus test for measuring a personal agent's learning capability.」
Quick overview
Recursive self-improvement requires agents to turn accumulated experience into better future behavior. Personal AI agents retain preferences, task histories, tool routines, and learned skills across sessions, making them an ideal setting for studying this capability—yet whether retained experience truly improves agents over time has not been systematically tested. PAST-Bench isolates this question through 26 scenarios and 204 episodes, comparing agents with experience retention on and off across memory, procedural reuse, information gathering, and update capabilities. Across seven base models and four agent frameworks, improvement is real but uneven. Guided by these findings, Hermes+ introduces five targeted interventions and raises average gains, strongest on tasks requiring outdated state to be replaced, though effects remain capability- and model-dependent.
- PAST-Bench features 26 scenarios and 204 episodes with matched on/off retention conditions to isolate recursive self-improvement.
- It evaluates memory, procedural reuse, information gathering, and update across seven base models and four agent frameworks.
- Improvement is real but uneven, and agents with similar headline gains can differ in whether those gains follow the intended pathway.
- Guided by findings, Hermes+ adds five targeted interventions, raising average gains and strongest on outdated-state-replacement tasks.
Full text
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