On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification

ID: 47215642
原文链接: https://arxiv.org/abs/2608.18066
PDF: https://arxiv.org/pdf/2608.18066v1
作者: Qinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang, Chien-Sheng Wu
日期: 2026-08-18
更新: 2026-08-18
分类: cs.AI, cs.CL, cs.LG
来源类型: arxiv
标签: agents, self-improvement, reliability, memory, arxiv
质量评分: 4/5
抓取时间: 2026-08-20T05:27:37Z

中文导读

基于记忆的自我改进智能体通过在线任务流维护文本记忆库来持续提升，但其可靠性此前很少被系统检验作者对两类记忆型方法做了扩展重评估：多次重复运行以量化方差，随机打乱任务以检验任务顺序的影响实验暴露出两个关键问题：性能方差可观结论对任务顺序高度敏感，说明现有自我改进方法的评估规范存在欠约束，单次运行得出的提升结论并不可靠

为什么值得关注

记忆型自我改进智能体的可靠性重评估：多次运行的方差与任务顺序效应表明单次运行的提升结论不可靠

Grounded note: the abstract reports two findings from a re-evaluation of two memory-based methods -- (1) agent evaluation is inherently noisy in complex environments and stacking a self-improving loop amplifies the noise, and (2) improvement depends heavily on task order, with default orderings acting as an implicit curriculum. The authors advocate multi-run reporting and stress-testing, and released code/data (see Comment).

关键信息

论文标题: On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
作者: Qinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang, Chien-Sheng Wu
arXiv: https://arxiv.org/abs/2608.18066
发布时间: 2026-08-18
arXiv 分类: cs.AI, cs.CL, cs.LG
Comment: Code: https://github.com/SalesforceAIResearch/self-improve-fragility Data: https://huggingface.co/datasets/Salesforce/self-improve-fragility
关联标签: agents, self-improvement, reliability, memory, arxiv

English Abstract

Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods have been critically overlooked. In this work, we conduct a comprehensive re-evaluation of two memory-based methods, broadening the scope of evaluation along two axes: (1) including multiple runs to quantify variance, and (2) randomly shuffling the tasks to investigate the effect of task order. Through these experiments, we make two observations that expose the fragility of current methods: First, agent evaluation is inherently noisy in complex environments and on multi-step tasks, and stacking a self-improving loop on top can further amplify this noise. Second, the agent's improvement is highly dependent on task order. Prior works often adopt default orderings that impose an implicit curriculum, acting as a hidden prerequisite for success. To better understand this fragility, we manually examine the agents' memory and hypothesize that task and environment underspecification contribute to this fragility. We validate this hypothesis by incorporating information that enables better specification, such as detailed rubrics and environment feedback, into the memory construction process. While this added information partially closes the performance degradation in previous experiments, significant gaps still remain, suggesting that other uncharacterized factors contribute to this fragility. Looking ahead, our work advocates for more rigorous evaluation protocols for self-improving agents by reporting results across multiple runs and stress-testing them under challenging conditions. Moreover, our findings on underspecification call for systems and interfaces that enable effective human oversight, preventing agents from failing in unforeseeable ways.

English Summary

Obsidian Notes

内容由 opencli arxiv paper 拉取 arXiv 元数据与摘要生成。
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