新闻
On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers found that memory-based self-improving agents are fragile, showing high variance across runs and strong dependence on task order during evaluation.
- 为何重要
- Engineers building or evaluating self-improving agents should care, especially when results seem inconsistent or depend on hidden task orderings.
- 注意
- Adding self-improvement loops amplifies noise in complex environments. Task underspecification remains even after adding rubrics and feedback, suggesting other uncharacterized factors.
收听本摘要
- agent
- rag
- eval
- self-improv
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