---
title: "On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.18066v1"
permalink: "https://feed.astralor.com/items/itm_4834099cf3a1da05c98908c0"
published: "2026-08-18T17:55:07.000Z"
score: 76
channel: "ai"
tags: ["arXiv","Evaluation Protocol","Memory Bank","Self-Improving Agents","Underspecification"]
---

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

## 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.

## At a glance

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.

## Key points

- 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

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.

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