# Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation

arXiv: https://arxiv.org/abs/2605.09315 · HTML: https://mlsanity.com/paper/2605.09315/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 10 May 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 22 of 22)

- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems](https://mlsanity.com/paper/2508.07407/) (2025)
- [Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory](https://mlsanity.com/paper/2511.20857/) (2025)
- [Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing](https://mlsanity.com/paper/2404.12253/) (2024)
- [Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents](https://mlsanity.com/paper/2509.26354/) (2025)
- [Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning](https://mlsanity.com/paper/2402.18865/) (2024)
- [EvoFlow: Evolving Diverse Agentic Workflows On The Fly](https://mlsanity.com/paper/2502.07373/) (2025)
- [EvoAgentX: An Automated Framework for Evolving Agentic Workflows](https://mlsanity.com/paper/2507.03616/) (2025)
- [Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data](https://mlsanity.com/paper/2602.21320/) (2026)
- [DynaSaur: Large Language Agents Beyond Predefined Actions](https://mlsanity.com/paper/2411.01747/) (2024)
- [B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners](https://mlsanity.com/paper/2412.17256/) (2024)
- [SEW: Self-Evolving Agentic Workflows for Automated Code Generation](https://mlsanity.com/paper/2505.18646/) (2025)
- [SWE-Bench-CL: Continual Learning for Coding Agents](https://mlsanity.com/paper/2507.00014/) (2025)
- [Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2601.18699/) (2026)
- [SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning](https://mlsanity.com/paper/2511.22367/) (2025)
- [Revisiting Weight Regularization for Low-Rank Continual Learning](https://mlsanity.com/paper/2602.17559/) (2026)

## Built on by (most cited first, 6 of 6)

- [PATH-Bench: Path-Dependent Evaluation of Lifelong Agents](https://mlsanity.com/paper/2608.01149/) (2026)
- [Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents](https://mlsanity.com/paper/2608.12851/) (2026)
- [Phantom Gains: Auditing Self-Improvement Against a Measured Null](https://mlsanity.com/paper/2608.20290/) (2026)
- [ADIAS: Automated Design of Interactive Agentic Systems](https://mlsanity.com/paper/2608.06410/) (2026)
- [D2ACCI: A Dual-Loop Diagnostic Protocol for Evidence-Preserving Agent Memory](https://mlsanity.com/paper/2608.17756/) (2026)
- [Sentry: Learning to Recover from LLM Agent Failures at Test Time](https://mlsanity.com/paper/2610.02994/) (2026)

## Abstract (arXiv)

Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and maintain persistent memory. However, we show that such self-evolution is often non-monotonic: adapting to new task distributions can progressively degrade previously acquired capabilities across all major evolution channels. We identify this phenomenon as capability erosion under self-evolution and show that it consistently emerges across workflow, skill, model, and memory evolution. To mitigate this issue, we propose Capability-Preserving Evolution (CPE), a general stabilization principle that constrains destructive capability drift during continual adaptation. Across all four evolution dimensions, CPE consistently improves retained capability stability while preserving adaptation performance. For example, in workflow evolution, CPE improves retained simple-task performance from 41.8% to 52.8% under GPT-5.1 optimization while simultaneously achieving stronger complex-task adaptation. Our findings suggest that stable long-horizon self-evolving agents require not only acquiring new capabilities, but also explicitly preserving previously learned ones during continual adaptation.