Self-Evolving Agents

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Self-evolving agents are systems that use experience from task execution to change how they solve future tasks, rather than relying only on a fixed model or hand-designed workflow. Common approaches store and refine reusable skills or memories, optimize prompts and agent harnesses, train policies with reinforcement learning, or co-evolve task generators, environments, and teams of agents. These methods are tested in settings such as web and GUI use, coding, embodied robotics, scientific workflows, and reasoning benchmarks, with feedback ranging from execution outcomes to verifiers and paired validation. A central challenge is making improvements transfer to new tasks without forgetting earlier abilities or introducing unsafe behavior; reliable evaluation, update verification, and long-term monitoring remain open problems.

Latest papers

427 papers104 papers in the last four weeks, up 148% on the four weeks before. 0.6% of all new papers.

ReCreate: Reasoning and Creating Domain Agents Driven by Experience
Jan 16, 2026Zhezheng Hao, Hong Wang, Jian Luo +6LLM Agents LLM Agent Self-Improvement
Zhejiang University · State Key Laboratory of Blockchain and Data Security, Zhejiang University · Tencent +3
PACEvolve: Enabling Progress-Aware Consistent Evolution
Jan 15, 2026Minghao Yan, Bo Peng, Benjamin Coleman +13Evolutionary Optimization Large Language Model-Guided Optimization
University of Wisconsin–Madison · Google · Google DeepMind +1
The Theory of Strategic Evolution: Three Principles of Strategic Replicators
Dec 5, 2025Kevin VallierSelf-Evolving Agents Evolutionary Game Theory
Institute of American Constitutional Thought and Leadership University of Toledo
Co-Evolving Agents: Learning from Failures as Hard Negatives
Nov 27, 2025Yeonsung Jung, Trilok Padhi, Sina Shaham +4Hard Negative Mining Self-Improving Agents
KAIST · Georgia State University · Independent Researcher +2
Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning
Nov 20, 2025Peng Xia, Kaide Zeng, Jiaqi Liu +5LLM Agent Self-Improvement Multi-Agent LLMs
UNC-Chapel Hill · Salesforce Research · Stanford University
SEDM: Selective Self-Evolving Distributed Memory for Agents
Sep 11, 2025Yichen Liu, Anna Min, Yinzhi Wang +14LLM Agent Memory Agent Memory Management
Gradient · Zhejiang University · Rice University
Generalising from Self-Produced Data: Model Training Beyond Human Constraints
Apr 7, 2025Alfath Daryl Alhajir, Jennifer Dodgson, Joseph Lim +4Large Language Model-Guided Optimization Self-Evolving Agents
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ReCreate: Reasoning and Creating Domain Agents Driven by Experience

Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang+5

Zhejiang University · State Key Laboratory of Blockchain and Data Security, Zhejiang University · Tencent+3

LLM Agents LLM Agent Self-Improvement Self-Evolving Agents LLM Agent Workflow Optimization

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Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, making their creation and adaptation across diverse domains labor-intensive. This situation poses a central question: can we automatically create and evolve domain agents through interaction experience in real-world environments? While recent approaches have sought to automate agent creation, several treat scaffold optimization as a black-box procedure guided primarily by final performance metrics. Such strategies overlook critical evidence explaining why an agent succeeds or fails and often incur high computational costs. To address these limitations, we propose ReCreate, an experience-driven framework for creating and evolving specialized domain agents. ReCreate systematically leverages agent interaction histories to identify the causes of success and failure and guide targeted scaffold updates. Specifically, we introduce an agent-as-optimizer paradigm in which an optimizer agent actively inspects execution experience and iteratively refines the task agent's scaffold through three key components: (i) an experience storage and retrieval mechanism for on-demand evidence inspection; (ii) a reasoning-creating synergy pipeline that translates execution evidence into updates to prompts, workflows, tools, and memory; and (iii) hierarchical updates that consolidate instance-level refinements into reusable domain-level improvements. Experiments across thirteen evaluation settings spanning four domains show that ReCreate consistently outperforms human-designed agents and existing automated agent generation and self-evolving baselines, even when evolving from minimal seed scaffolds.