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Life sciences · Preprint
arXiv · August 18, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint documents substantial fragility in memory-based self-improving agents: performance exhibits high variance across runs, is strongly dependent on task order (which prior work left uncontrolled), and degrades unpredictably in multi-step tasks. The authors hypothesize that task and environment underspecification drives this fragility and show that adding rubrics and feedback partially—but incompletely—restores performance.
Empirical re-evaluation with variance quantification and task-order perturbation. Memory-based self-improving agents evaluated on online task streams and multi-step tasks in complex environments.. Intervention: Incorporation of detailed rubrics and environment feedback into memory construction process.. Compared with: Baseline memory construction without enhanced specification (prior default task orderings)..
Agent evaluation in complex environments is inherently noisy; stacking a self-improving loop amplifies this noise. Agent improvement is highly dependent on task order; prior works used default orderings that act as implicit curriculum. Adding detailed rubrics and environment feedback partially closes performance degradation but significant gaps remain.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
A re-evaluation study of existing methods identifying robustness issues through variance and task-order testing, without proposing a validated solution; raises important methodological concerns but lacks the definitive evidence needed for practice change.
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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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