On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
Score
76首次系统性揭示自我改进智能体对随机种子和任务顺序的敏感性问题，信息增量显著。
实验设计严谨，结合定量评估与人工记忆检查，论证链条完整。
为智能体评估提供可操作建议，并启发人类监督接口设计，实用价值高。
紧抓当前Agent自我改进热潮，对可靠性问题具有即时警示意义。
发布于arXiv预印本平台，尚未经同行评审，但学术规范性和可复现性良好。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
Tackles a timely Agent research topic, systematically quantifying variance and task order effects, with strong methodological value.
AI commentary
「This is a must-read for anyone building self-improving agents — simple experiments (multiple runs + shuffled order) puncture the illusion of stable improvement. For teams developing memory-based agents, results without variance and order sensitivity reports are no longer convincing; for benchmark designers, it's a clear methodological wake-up call.」
Quick overview
Memory-based self-improving agents, which learn from an online stream of tasks and maintain a textual memory bank, have shown promise but their reliability is critically overlooked. This paper re-evaluates two memory-based methods by running multiple trials to quantify variance and randomly shuffling task order to probe order effects. Two observations expose fragility: (1) agent evaluation is inherently noisy in complex multi-step environments, and self-improvement loops amplify this noise; (2) improvement depends heavily on task order, with default orderings acting as a hidden curriculum and implicit prerequisite for success. Manual inspection of agent memory suggests task and environment underspecification contribute to fragility. Adding detailed rubrics and environment feedback during memory construction partially closes performance gaps but significant gaps remain, indicating other uncharacterized factors. The work advocates for rigorous evaluation protocols that report multiple runs and stress-test under challenging conditions, and calls for systems enabling effective human oversight to prevent unforeseeable failures.
- Re-evaluates two memory-based self-improving agents across multiple runs and shuffled task orders, exposing reliability issues
- Self-improvement loops amplify evaluation noise in complex multi-step tasks; improvement is highly dependent on task order
- Task and environment underspecification contribute to fragility; adding rubrics and environment feedback only partially closes gaps
- Calls for rigorous evaluation protocols (multiple runs, stress tests) and designs for effective human oversight
Full text
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