On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
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 self-improving runs to quantify variance, and (2) 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 e...
| Authors | Qinyuan Ye, Yu Li, Yada Pruksachatkun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 August 2026 |
| Last updated at source | 6 September 2026 |
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