A curated and continuously updated collection of research papers on agentic evolution — persistent, system-level change in agentic systems through autonomous or human-involved selective pressure.
Some papers appear under multiple substrates when they contribute to more than one.
This paper list accompanies a survey paper on agentic evolution.
Evolutionary Substrate — what component of the agentic system evolves:
- Cortex (Π): The deliberative substrate — base LLM weights, prompts, decoding strategies, and inference-time procedures
- Action (A): The executive substrate — toolset, execution logic (workflow graphs, control flow), and multi-agent orchestration
- Memory & Sense (M): The epistemic substrate — episodic/semantic/procedural memory, retention and retrieval policies, perception encoders, and world models
Consolidation Pathway — how experience becomes persistent change:
ΔStructural: discrete, symbolic edits to non-parametric artifacts (prompts, workflow graphs, memory indices, tool configurations, or code)∇Parametric: continuous updates to model weight parametersΔ+∇Hybrid: both structural and parametric consolidation within a single system
Selective Pressure — what drives evolution:
H=0Autonomous: selective pressure from environmental experience (including formal verification) and self-generated signals (self-play and self-rewarding)H≠0Human-involved: human input shapes the evolutionary trajectory (prescriptions, demonstrations, evaluative feedback, interactive collaboration, or implicit signals)
- "Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs"
Liu et al. arXiv 2026. [paper] - "Self-Optimizing Multi-Agent Systems for Deep Research"
Câmara et al. arXiv 2026. [paper] - "Combee: Scaling Prompt Learning for Self-Improving Language Model Agents"
Li et al. arXiv 2026. [paper] - "Self-Evolving LLM Memory Extraction Across Heterogeneous Tasks"
Yang et al. arXiv 2026. [paper] - "Evolutionary Context Search for Automated Skill Acquisition"
Sun et al. arXiv 2026. [paper] - "Understanding the Challenges in Iterative Generative Optimization with LLMs"
Nie et al. arXiv 2026. [paper] - "JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents"
Ghoshal et al. arXiv 2026. [paper] - "Optimizing Generative AI by Backpropagating Language Model Feedback"
Yuksekgonul et al. Nature 2025. [paper] - "REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization"
Zhang et al. ICML 2025. [paper] - "Large Language Models as Optimizers"
Yang et al. ICLR 2024. [paper] - "Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers"
Guo et al. ICLR 2024. [paper] - "Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution"
Fernando et al. ICML 2024. [paper] - "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines"
Khattab et al. ICLR 2024. [paper] - "Large Language Models Are Human-Level Prompt Engineers"
Zhou et al. ICLR 2023. [paper]
- "Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models"
Zhang et al. ICLR 2026. [paper] - "Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills"
Ni et al. arXiv 2026. [paper] - "Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts"
Li et al. arXiv 2026. [paper] - "Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning"
Zhuang et al. arXiv 2026. [paper] - "AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents"
Wang et al. arXiv 2026. [paper] - "Harnessing Pre-Resolution Signals for Future Prediction Agents"
Wei et al. arXiv 2026. [paper] - "ContraPrompt: Contrastive Prompt Optimization via Dyadic Reasoning Trace Analysis"
Rishav et al. arXiv 2026. [paper] - "AEL: Agent Evolving Learning for Open-Ended Environments"
Xu et al. arXiv 2026. [paper] - "Discovering Agentic Safety Specifications from 1-Bit Danger Signals"
Gallego ALA Workshop @ AAMAS 2026. [paper] - "EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale"
Zhu et al. arXiv 2026. [paper] - "Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery"
Wainrib et al. arXiv 2026. [paper] - "STELLA: Self-Evolving LLM Agent for Biomedical Research"
Jin et al. arXiv 2025. [paper] - "Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization"
Zhang et al. ACL 2024. [paper] - "Reflexion: Language Agents with Verbal Reinforcement Learning"
Shinn et al. NeurIPS 2023. [paper]
- "Evolving Interpretable Constitutions for Multi-Agent Coordination"
Kumar et al. arXiv 2026. [paper] - "SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering"
Gong et al. arXiv 2026. [paper] - "Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization"
Huang et al. arXiv 2026. [paper] - "A Self-Evolving Agentic Framework for Metasurface Inverse Design"
Huang et al. arXiv 2026. [paper] - "Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents"
Tian arXiv 2026. [paper] - "MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games"
Xie et al. arXiv 2026. [paper] - "EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms"
Yuan et al. NAACL 2025. [paper] - "Evolving Excellence: Automated Optimization of LLM-based Agents"
Brookes et al. arXiv 2025. [paper]
- "Meta-Harness: End-to-End Optimization of Model Harnesses"
Lee et al. arXiv 2026. [paper] - "M★: Every Task Deserves Its Own Memory Harness"
Pan et al. arXiv 2026. [paper] - "AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering"
Zhang arXiv 2026. [paper] - "Autogenesis: A Self-Evolving Agent Protocol"
Zhang et al. arXiv 2026. [paper] - "Automated Design of Agentic Systems"
Hu et al. ICLR 2025. [paper] - "A Self-Improving Coding Agent"
Robeyns et al. arXiv 2025. [paper] - "Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents"
Zhang et al. arXiv 2025. [paper] - "Symbolic Learning Enables Self-Evolving Agents"
Zhou et al. AI Open 2025. [paper]
- "LangMARL: Natural Language Multi-Agent Reinforcement Learning"
Yao et al. arXiv 2026. [paper] - "Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization"
He et al. arXiv 2026. [paper] - "EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines"
Zhang et al. arXiv 2026. [paper]
- "Prompt Optimization with Human Feedback"
Lin et al. arXiv 2024. [paper]
- "ReMiT: RL-Guided Mid-Training for Iterative LLM Evolution"
Huang et al. arXiv 2026. [paper] - "Agentic Critical Training"
Liu et al. arXiv 2026. [paper] - "SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization"
Lu et al. arXiv 2026. [paper] - "RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems"
Qu et al. ICLR 2026. [paper] - "Expanding the Capabilities of Reinforcement Learning via Text Feedback"
Song et al. arXiv 2026. [paper] - "Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future"
Wang et al. arXiv 2025. [paper] - "Process-based Self-Rewarding Language Models"
Zhang et al. Findings of ACL 2025. [paper] - "Language Models that Think, Chat Better"
Bhaskar et al. arXiv 2025. [paper] - "The Art of Scaling Reinforcement Learning Compute for LLMs"
Khatri et al. arXiv 2025. [paper] - "Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning"
Hübotter et al. arXiv 2025. [paper] - "SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data"
Fang et al. arXiv 2025. [paper] - "Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation"
Li et al. arXiv 2025. [paper] - "Self-Rewarding Language Models"
Yuan et al. ICML 2024. [paper] - "STaR: Bootstrapping Reasoning With Reasoning"
Zelikman et al. NeurIPS 2022. [paper]
- "SAGE: Multi-Agent Self-Evolution for LLM Reasoning"
Peng et al. arXiv 2026. [paper] - "MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data"
Li et al. arXiv 2026. [paper] - "SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments"
Li et al. arXiv 2026. [paper] - "One Model, All Roles: Multi-Turn, Multi-Agent Self-Play Reinforcement Learning for Conversational Social Intelligence"
Jiang et al. arXiv 2026. [paper] - "SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning"
Liu et al. ICLR 2026. [paper] - "Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation"
Sui et al. arXiv 2026. [paper] - "Vision-Zero: Scalable VLM Self-Evolution via Multi-Agent Self-Play"
Wang et al. ICLR 2026. [paper] - "Dr. Zero: Self-Evolving Search Agents without Training Data"
Yue et al. arXiv 2026. [paper] - "The Alignment Waltz: Jointly Training Agents to Collaborate for Safety"
Zhang et al. ICLR 2026. [paper] - "SELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression"
Zhang et al. arXiv 2026. [paper] - "Multi-Agent Evolve: LLM Self-Improve through Co-evolution"
Chen et al. arXiv 2025. [paper] - "R-Zero: Self-Evolving Reasoning LLM from Zero Data"
Huang et al. arXiv 2025. [paper] - "MARS: Co-evolving Dual-System Deep Research via Multi-Agent Reinforcement Learning"
Chen et al. arXiv 2025. [paper] - "CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards"
Xue et al. arXiv 2025. [paper] - "MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs"
Yuan et al. arXiv 2025. [paper] - "Toward Training Superintelligent Software Agents through Self-Play SWE-RL"
Wei et al. arXiv 2025. [paper] - "Absolute Zero: Reinforced Self-play Reasoning with Zero Data"
Zhao et al. arXiv 2025. [paper] - "Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning"
Xia et al. arXiv 2025. [paper]
- "Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking"
Beigi et al. arXiv 2026. [paper]
- "AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis"
Chen et al. ICLR 2026. [paper] - "Diving into Self-Evolving Training for Multimodal Reasoning"
Liu et al. ICML 2025. [paper] - "MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct"
Luo et al. Findings of ACL 2025. [paper] - "Skill-Targeted Adaptive Training"
He et al. arXiv 2025. [paper] - "ALAS: Autonomous Learning Agent for Self-Updating Language Models"
Atreja arXiv 2025. [paper] - "NExT: Teaching Large Language Models to Reason about Code Execution"
Ni et al. ICML 2024. [paper]
- "SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents"
Feng et al. arXiv 2026. [paper] - "Computer Environments Elicit General Agentic Intelligence in LLMs"
Cheng et al. arXiv 2026. [paper] - "GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning"
Li et al. arXiv 2026. [paper] - "TRACE: Capability-Targeted Agentic Training"
Kang et al. arXiv 2026. [paper] - "Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning"
Dong et al. arXiv 2025. [paper] - "ReTool: Reinforcement Learning for Strategic Tool Use in LLMs"
Feng et al. arXiv 2025. [paper] - "VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use"
Jiang et al. arXiv 2025. [paper] - "ToolRL: Reward is All Tool Learning Needs"
Qian et al. arXiv 2025. [paper] - "START: Self-taught Reasoner with Tools"
Li et al. arXiv 2025. [paper] - "rStar2-Agent: Agentic Reasoning Technical Report"
Shang et al. arXiv 2025. [paper] - "Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning"
Zhang et al. arXiv 2025. [paper] - "Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use"
Zhang et al. arXiv 2025. [paper] - "ACECODER: Acing Coder RL via Automated Test-Case Synthesis"
Zeng et al. ACL 2025. [paper] - "One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents"
Zhang et al. arXiv 2025. [paper] - "ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs"
Qin et al. ICLR 2024. [paper] - "ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving"
Gou et al. ICLR 2024. [paper] - "StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback"
Dou et al. ACL 2024. [paper]
- "Safe and Scalable Web Agent Learning via Recreated Websites"
Chae et al. arXiv 2026. [paper] - "UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience"
Lin et al. arXiv 2026. [paper] - "Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence"
Dong et al. arXiv 2026. [paper] - "Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning"
Qin et al. ICLR 2026. [paper] - "Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents"
Cai et al. arXiv 2026. [paper] - "CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection"
Liu et al. arXiv 2026. [paper] - "WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model"
Fang et al. EMNLP 2025. [paper] - "WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning"
Qi et al. ICLR 2025. [paper] - "Reinforcement Learning for Long-Horizon Interactive LLM Agents"
Chen et al. arXiv 2025. [paper]
- "RAGEN-2: Reasoning Collapse in Agentic RL"
Wang et al. arXiv 2026. [paper] - "Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis"
Zhai et al. arXiv 2026. [paper] - "On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM Agents"
Zou et al. arXiv 2026. [paper] - "When Reward Hacking Rebounds: Understanding and Mitigating It with Representation-Level Signals"
Wu et al. arXiv 2026. [paper] - "Accordion-Thinking: Self-Regulated Step Summaries for Efficient and Readable LLM Reasoning"
Yang et al. arXiv 2026. [paper] - "MemoBrain: Executive Memory as an Agentic Brain for Reasoning"
Qian et al. arXiv 2026. [paper] - "A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning"
Wang et al. arXiv 2025. [paper] - "RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning"
Wang et al. arXiv 2025. [paper] - "WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning"
Wei et al. EMNLP 2025. [paper] - "AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning"
Xi et al. arXiv 2025. [paper] - "MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent"
Yu et al. arXiv 2025. [paper] - "MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents"
Zhou et al. arXiv 2025. [paper] - "CollabLLM: From Passive Responders to Active Collaborators"
Wu et al. ICML 2025. [paper] - "AgentGym: Evolving Large Language Model-based Agents across Diverse Environments"
Xi et al. arXiv 2024. [paper]
- "SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation"
Kim et al. arXiv 2026. [paper] - "PersonaVLM: Long-Term Personalized Multimodal LLMs"
Nie et al. arXiv 2026. [paper] - "Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning"
Hu et al. arXiv 2026. [paper] - "PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory"
Jiang et al. arXiv 2025. [paper] - "Fed-SE: Federated Self-Evolution for Cross-Environment Knowledge Transfer in Privacy-Constrained LLM Agents"
Chen et al. arXiv 2025. [paper] - "Self-Updatable Large Language Models by Integrating Context into Model Parameters"
Wang et al. ICLR 2025. [paper]
- "ATLAS: A Multi-LLM Training Framework for EvoDPO with Adaptive Reference Evolution"
Jeon et al. arXiv 2026. [paper] - "CoLa: Learning to Interactively Collaborate with Large Language Models"
Sharma et al. arXiv 2025. [paper] - "AutoFlow: Automated Workflow Generation for Large Language Model Agents"
Li et al. arXiv 2024. [paper]
- "End-to-End Test-Time Training for Long Context"
Tandon et al. arXiv 2025. [paper] - "Self-Adapting Language Models"
Zweiger et al. arXiv 2025. [paper]
- "Discovering Novel LLM Experts via Task-Capability Coevolution"
Dai et al. ICLR 2026. [paper] - "Evolutionary Policy Optimization"
Wang et al. arXiv 2025. [paper]
- "EVA: Efficient Reinforcement Learning for End-to-End Video Agent"
Zhang et al. CVPR 2026. [paper] - "FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing"
Zhang et al. arXiv 2026. [paper] - "Co-Evolution of Policy and Internal Reward for Language Agents"
Wang et al. arXiv 2026. [paper] - "FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution"
Zou et al. arXiv 2026. [paper] - "Meta-Reinforcement Learning with Self-Reflection for Agentic Search"
Xiao et al. arXiv 2026. [paper] - "MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems"
Ye et al. ICML 2025. [paper] - "Automated Skill Discovery for Language Agents through Exploration and Iterative Feedback"
Yang et al. arXiv 2025. [paper] - "π0: A Vision-Language-Action Flow Model for General Robot Control"
Black et al. RSS 2025. [paper] - "Towards Agentic Self-Learning LLMs in Search Environment"
Sun et al. arXiv 2025. [paper] - "Scaling Agent Learning via Experience Synthesis"
Chen et al. arXiv 2025. [paper] - "OMNI: Open-endedness via Models of human Notions of Interestingness"
Zhang et al. ICLR 2024. [paper] - "Agent Lumos: Unified and Modular Training for Open-Source Language Agents"
Yin et al. ACL 2024. [paper] - "Open-Ended Learning Leads to Generally Capable Agents"
Team et al. arXiv 2021. [paper]
- "Mastering Diverse Control Tasks through World Models"
Hafner et al. Nature 2025. [paper] - "EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks"
Feng et al. arXiv 2025. [paper] - "Recurrent World Models Facilitate Policy Evolution"
Ha et al. NeurIPS 2018. [paper]
- "Learning to Summarize User Information for Personalized Reinforcement Learning from Human Feedback"
Nam et al. ICLR 2026. [paper] - "Pioneer Agent: Continual Improvement of Small Language Models in Production"
Atreja et al. arXiv 2026. [paper] - "Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback"
Yang et al. arXiv 2025. [paper] - "Personalized Language Modeling from Personalized Human Feedback"
Li et al. arXiv 2024. [paper] - "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback"
Abramson et al. arXiv 2022. [paper] - "Interactive Learning from Policy-Dependent Human Feedback"
MacGlashan et al. ICML 2017. [paper] - "Learning Preferences for Manipulation Tasks from Online Coactive Feedback"
Jain et al. The International Journal of Robotics Research 2015. [paper] - "Coactive Learning"
Shivaswamy et al. Journal of Artificial Intelligence Research 2015. [paper]
- "Memory Intelligence Agent"
Qiao et al. arXiv 2026. [paper] - "Experiential Reinforcement Learning"
Shi et al. arXiv 2026. [paper] - "VERDICT: Verifiable Evolving Reasoning with Directive-Informed Collegial Teams for Legal Judgment Prediction"
Liao et al. arXiv 2026. [paper] - "Evaluation-driven Scaling for Scientific Discovery"
Ye et al. arXiv 2026. [paper] - "DeltaMem: Towards Agentic Memory Management via Reinforcement Learning"
Zhang et al. arXiv 2026. [paper] - "Self-Consolidation for Self-Evolving Agents"
Yu et al. arXiv 2026. [paper] - "Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization"
Shi et al. arXiv 2025. [paper]
- "SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning"
Xia et al. arXiv 2026. [paper] - "SLEA-RL: Step-Level Experience Augmented Reinforcement Learning for Multi-Turn Agentic Training"
Wang et al. arXiv 2026. [paper] - "CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution"
Yang et al. arXiv 2026. [paper] - "RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback"
Zhang et al. arXiv 2026. [paper] - "Evolutionary System Prompt Learning for Reinforcement Learning in LLMs"
Zhang et al. arXiv 2026. [paper] - "SkillOS: Learning Skill Curation for Self-Evolving Agents"
Ouyang et al. arXiv 2026. [paper] - "SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations"
Ali et al. arXiv 2026. [paper] - "SEVerA: Verified Self-Evolving Agents"
Banerjee et al. arXiv 2026. [paper] - "SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology"
Nie et al. arXiv 2026. [paper] - "EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering"
Huang et al. arXiv 2026. [paper] - "AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization"
Yuan et al. arXiv 2026. [paper] - "HELIX: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving"
Su et al. ICLR 2026. [paper] - "Reinforcement Learning for Self-Improving Agent with Skill Library"
Wang et al. arXiv 2025. [paper] - "EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle"
Wu et al. arXiv 2025. [paper] - "AgentEvolver: Towards Efficient Self-Evolving Agent System"
Zhai et al. arXiv 2025. [paper] - "EvolveSearch: An Iterative Self-Evolving Search Agent"
Zhang et al. arXiv 2025. [paper] - "Agentic Policy Optimization via Instruction-Policy Co-Evolution"
Zhou et al. arXiv 2025. [paper] - "InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios"
Yu et al. arXiv 2025. [paper]
- "PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor"
Yang et al. arXiv 2026. [paper] - "Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks"
Wu et al. arXiv 2026. [paper] - "S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Research"
Team arXiv 2026. [paper] - "Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration"
Zhang et al. arXiv 2026. [paper] - "Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning"
Yan et al. arXiv 2025. [paper]
- "Optimizing Agentic Workflows using Meta-tools"
Abuzakuk et al. arXiv 2026. [paper] - "Mem^2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation"
Cheng et al. arXiv 2026. [paper] - "Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks"
Li et al. arXiv 2026. [paper] - "RefTool: Reference-Guided Tool Creation for Knowledge-Intensive Reasoning"
Liu et al. ICLR 2026. [paper] - "OpenGame: Open Agentic Coding for Games"
Jiang et al. arXiv 2026. [paper] - "GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)"
Liang et al. arXiv 2026. [paper] - "SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support"
Liu et al. arXiv 2026. [paper] - "Memento-Skills: Let Agents Design Agents"
Zhou et al. arXiv 2026. [paper] - "CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery"
Qu et al. arXiv 2026. [paper] - "SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources"
Shen et al. arXiv 2026. [paper] - "Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning"
Shen et al. arXiv 2026. [paper] - "SkillX: Automatically Constructing Skill Knowledge Bases for Agents"
Wang et al. arXiv 2026. [paper] - "El Agente Forjador: Task-Driven Agent Generation for Quantum Simulation"
Zhang et al. arXiv 2026. [paper] - "AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse"
Zhang et al. arXiv 2026. [paper] - "CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification"
Zhang et al. arXiv 2026. [paper] - "STELLA: Self-Evolving LLM Agent for Biomedical Research"
Jin et al. arXiv 2025. [paper] - "From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions"
Qu et al. ICLR 2025. [paper] - "Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution"
Qiu et al. arXiv 2025. [paper] - "Alita-G: Self-Evolving Generative Agent for Agent Generation"
Qiu et al. arXiv 2025. [paper] - "Voyager: An Open-Ended Embodied Agent with Large Language Models"
Wang et al. TMLR 2024. [paper]
- "AVO: Agentic Variation Operators for Autonomous Evolutionary Search"
Chen et al. arXiv 2026. [paper] - "PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs"
Gungordu et al. arXiv 2026. [paper] - "AIRA_2: Overcoming Bottlenecks in AI Research Agents"
Hambardzumyan et al. arXiv 2026. [paper] - "Digital Red Queen: Adversarial Program Evolution in Core War with LLMs"
Kumar et al. arXiv 2026. [paper] - "OR-Agent: Bridging Evolutionary Search and Structured Research for Automated Algorithm Discovery"
Liu et al. arXiv 2026. [paper] - "AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection"
Ray et al. arXiv 2026. [paper] - "Evaluation-driven Scaling for Scientific Discovery"
Ye et al. arXiv 2026. [paper] - "AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models"
Gautam et al. arXiv 2026. [paper] - "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery"
Novikov et al. arXiv 2025. [paper] - "AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization"
Zhang et al. arXiv 2025. [paper] - "Mathematical Discoveries from Program Search with Large Language Models"
Romera-Paredes et al. Nature 2024. [paper] - "Evolution through Large Models"
Lehman et al. arXiv 2022. [paper]
- "Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization"
He et al. arXiv 2026. [paper] - "Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization"
Huang et al. arXiv 2026. [paper] - "Meta-Harness: End-to-End Optimization of Model Harnesses"
Lee et al. arXiv 2026. [paper] - "Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses"
Lin et al. arXiv 2026. [paper] - "Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts"
Li et al. arXiv 2026. [paper] - "HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning"
Xu et al. arXiv 2026. [paper] - "Autogenesis: A Self-Evolving Agent Protocol"
Zhang et al. arXiv 2026. [paper] - "EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines"
Zhang et al. arXiv 2026. [paper] - "Automated Design of Agentic Systems"
Hu et al. ICLR 2025. [paper] - "SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning"
Li et al. arXiv 2025. [paper] - "AFlow: Automating Agentic Workflow Generation"
Zhang et al. ICLR 2025. [paper] - "Symbolic Learning Enables Self-Evolving Agents"
Zhou et al. AI Open 2025. [paper]
- "AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents"
Wang et al. arXiv 2026. [paper] - "Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing"
Weng et al. arXiv 2026. [paper] - "ASI-Evolve: AI Accelerates AI"
Xu et al. arXiv 2026. [paper] - "Learning to Continually Learn via Meta-learning Agentic Memory Designs"
Xiong et al. arXiv 2026. [paper] - "AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering"
Zhang arXiv 2026. [paper] - "A Self-Improving Coding Agent"
Robeyns et al. arXiv 2025. [paper] - "Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents"
Zhang et al. arXiv 2025. [paper]
- "Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABC"
Yu et al. arXiv 2026. [paper] - "Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents"
Wang et al. arXiv 2026. [paper]
- "JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents"
Ghoshal et al. arXiv 2026. [paper] - "Symphony-Coord: Emergent Coordination in Decentralized Agent Systems"
Guan et al. arXiv 2026. [paper] - "Evolving Excellence: Automated Optimization of LLM-based Agents"
Brookes et al. arXiv 2025. [paper]
- "Position: Agentic Evolution is the Path to Evolving LLMs"
Lin et al. arXiv 2026. [paper] - "Understanding the Challenges in Iterative Generative Optimization with LLMs"
Nie et al. arXiv 2026. [paper]
- "SkillClaw: Let Skills Evolve Collectively with Agentic Evolver"
Ma et al. arXiv 2026. [paper] - "EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation"
Zhang et al. arXiv 2026. [paper] - "Leveraging LLMs for Dynamic IoT Systems Generation through Mixed-Initiative Interaction"
Adnan et al. ICSA-C 2025. [paper] - "Democratizing AI scientists using ToolUniverse"
Gao et al. arXiv 2025. [paper]
- "Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning"
Jiang et al. arXiv 2026. [paper] - "π0: A Vision-Language-Action Flow Model for General Robot Control"
Black et al. RSS 2025. [paper] - "RouteLLM: Learning to Route LLMs with Preference Data"
Ong et al. ICLR 2025. [paper]
- "SEVerA: Verified Self-Evolving Agents"
Banerjee et al. arXiv 2026. [paper] - "Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence"
Dong et al. arXiv 2026. [paper] - "SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents"
Feng et al. arXiv 2026. [paper] - "EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering"
Huang et al. arXiv 2026. [paper] - "SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology"
Nie et al. arXiv 2026. [paper] - "HELIX: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving"
Su et al. ICLR 2026. [paper] - "Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks"
Wu et al. arXiv 2026. [paper] - "SkillOS: Learning Skill Curation for Self-Evolving Agents"
Ouyang et al. arXiv 2026. [paper] - "Reinforcement Learning for Self-Improving Agent with Skill Library"
Wang et al. arXiv 2025. [paper] - "InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios"
Yu et al. arXiv 2025. [paper] - "AutoFlow: Automated Workflow Generation for Large Language Model Agents"
Li et al. arXiv 2024. [paper]
- "AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents"
Bao et al. arXiv 2026. [paper] - "Mem^2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation"
Cheng et al. arXiv 2026. [paper] - "KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning"
Dong et al. arXiv 2026. [paper] - "Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement"
Fang et al. arXiv 2026. [paper] - "AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models"
Gautam et al. arXiv 2026. [paper] - "OpenGame: Open Agentic Coding for Games"
Jiang et al. arXiv 2026. [paper] - "Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents"
Kim et al. arXiv 2026. [paper] - "Experience Transfer for Multimodal LLM Agents in Minecraft Game"
Li et al. arXiv 2026. [paper] - "Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts"
Li et al. arXiv 2026. [paper] - "Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates"
Li et al. arXiv 2026. [paper] - "Learning to Commit: Generating Organic Pull Requests via Online Repository Memory"
Li et al. arXiv 2026. [paper] - "Towards Self-Improving Error Diagnosis in Multi-Agent Systems"
Li et al. arXiv 2026. [paper] - "GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)"
Liang et al. arXiv 2026. [paper] - "Position: Agentic Evolution is the Path to Evolving LLMs"
Lin et al. arXiv 2026. [paper] - "EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery"
Lyu et al. arXiv 2026. [paper] - "ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory"
Ouyang et al. ICLR 2026. [paper] - "MAPLE: Multi-Agent Adaptive Planning with Long-Term Memory for Table Reasoning"
Bai et al. ALTA 2025. [paper] - "Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark"
Cai et al. arXiv 2025. [paper] - "FLEX: Continuous Agent Evolution via Forward Learning from Experience"
Cai et al. arXiv 2025. [paper] - "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution"
Cao et al. arXiv 2025. [paper] - "MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation"
Chen et al. arXiv 2025. [paper] - "Memp: Exploring Agent Procedural Memory"
Fang et al. arXiv 2025. [paper] - "Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks"
Sarukkai et al. arXiv 2025. [paper] - "Agent Workflow Memory"
Wang et al. ICML 2025. [paper] - "Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory"
Wei et al. arXiv 2025. [paper] - "Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents"
Li et al. arXiv 2024. [paper] - "ToolChain: Efficient Action Space Navigation in Large Language Models with A Search"**
Zhuang et al. ICLR 2024. [paper]
- "Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization"
Wang et al. arXiv 2026. [paper] - "ELITE: Experiential Learning and Intent-Aware Transfer for Self-improving Embodied Agents"
Wei et al. arXiv 2026. [paper] - "MineEvolve: Self-Evolution with Accumulated Knowledge for Long-Horizon Embodied Minecraft Agents"
Xie et al. arXiv 2026. [paper]
- "Self-Evolving Multi-Agent Framework for Efficient Decision Making in Real-Time Strategy Scenarios"
Ma et al. arXiv 2026. [paper] - "MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games"
Xie et al. arXiv 2026. [paper] - "Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy"
Guan et al. NeurIPS 2024. [paper]
- "GSEM: Graph-based Self-Evolving Memory for Experience Augmented Clinical Reasoning"
Han et al. arXiv 2026. [paper] - "OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence"
Liu et al. arXiv 2026. [paper] - "Emulating Clinician Cognition via Self-Evolving Deep Clinical Research"
Ren et al. arXiv 2026. [paper] - "Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve"
Shen et al. arXiv 2026. [paper] - "PhysNote: Self-Knowledge Notes for Evolvable Physical Reasoning in Vision-Language Model"
Zhang et al. arXiv 2026. [paper] - "HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research"
Zhu et al. arXiv 2025. [paper]
- "CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery"
Qu et al. arXiv 2026. [paper] - "AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents"
Wang et al. arXiv 2026. [paper] - "Forage V2: Knowledge Evolution and Transfer in Autonomous Agent Organizations"
Xie arXiv 2026. [paper] - "G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems"
Zhang et al. arXiv 2025. [paper]
- "OR-Agent: Bridging Evolutionary Search and Structured Research for Automated Algorithm Discovery"
Liu et al. arXiv 2026. [paper] - "AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model"
Long arXiv 2026. [paper] - "ASI-Evolve: AI Accelerates AI"
Xu et al. arXiv 2026. [paper]
- "EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution"
He et al. arXiv 2026. [paper] - "Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization"
Kwon et al. ICLR 2026. [paper] - "Choosing How to Remember: Adaptive Memory Structures for LLM Agents"
Lu et al. arXiv 2026. [paper] - "GAM: Hierarchical Graph-based Agentic Memory for LLM Agents"
Wu et al. arXiv 2026. [paper] - "Lightweight LLM Agent Memory with Small Language Models"
Zhang et al. ACL 2026. [paper] - "To Know is to Construct: Schema-Constrained Generation for Agent Memory"
Zheng et al. arXiv 2026. [paper] - "HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents"
Zhu et al. arXiv 2026. [paper] - "What Deserves Memory: Adaptive Memory Distillation for LLM Agents"
Ma et al. arXiv 2025. [paper] - "A-MEM: Agentic Memory for LLM Agents"
Xu et al. NeurIPS 2025. [paper] - "Generative Agents: Interactive Simulacra of Human Behavior"
Park et al. UIST 2023. [paper]
- "MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution"
Lin et al. arXiv 2026. [paper] - "PRIME: Training Free Proactive Reasoning via Iterative Memory Evolution for User-Centric Agent"
Wang et al. arXiv 2026. [paper] - "Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback"
Zhang et al. arXiv 2026. [paper] - "EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines"
Zhang et al. arXiv 2026. [paper] - "Memento 2: Learning by Stateful Reflective Memory"
Wang arXiv 2025. [paper] - "AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization"
Zhang et al. arXiv 2025. [paper]
- "ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems"
Bering arXiv 2026. [paper] - "FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory"
Gu et al. arXiv 2026. [paper] - "When to Forget: A Memory Governance Primitive"
Simsek arXiv 2026. [paper] - "AEL: Agent Evolving Learning for Open-Ended Environments"
Xu et al. arXiv 2026. [paper]
- "Prism: An Evolutionary Memory Substrate for Multi-Agent Open-Ended Discovery"
Mishra arXiv 2026. [paper] - "M★: Every Task Deserves Its Own Memory Harness"
Pan et al. arXiv 2026. [paper] - "MemEvolve: Meta-Evolution of Agent Memory Systems"
Zhang et al. arXiv 2025. [paper]
- "Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems"
Feng et al. TMLR 2026. [paper] - "ROZA Graphs: Self-Improving Near-Deterministic RAG through Evidence-Centric Feedback"
Penaroza arXiv 2026. [paper] - "Latent Preference Modeling for Cross-Session Personalized Tool Calling"
Yoon et al. arXiv 2026. [paper] - "Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models"
Yang et al. NeurIPS 2024. [paper]
- "A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression"
Ren et al. arXiv 2026. [paper]
- "Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo"
Gadzhiev et al. arXiv 2026. [paper] - "PersonaVLM: Long-Term Personalized Multimodal LLMs"
Nie et al. arXiv 2026. [paper] - "Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory"
Chhikara et al. arXiv 2025. [paper] - "Zep: A Temporal Knowledge Graph Architecture for Agent Memory"
Rasmussen et al. arXiv 2025. [paper] - "RGMem: Renormalization Group-inspired Memory Evolution for Language Agents"
Tian et al. arXiv 2025. [paper] - "Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles"
Westhäusser et al. arXiv 2025. [paper] - "MemoryBank: Enhancing Large Language Models with Long-Term Memory"
Zhong et al. AAAI 2024. [paper] - "MemGPT: Towards LLMs as Operating Systems"
Packer et al. arXiv 2023. [paper]
- "SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis"
Chen et al. arXiv 2026. [paper] - "Your Code Agent Can Grow Alongside You with Structured Memory"
Deng et al. arXiv 2026. [paper] - "EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation"
Zhang et al. arXiv 2026. [paper] - "Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance"
He et al. EMNLP 2025. [paper] - "Magentic-UI: Towards Human-in-the-loop Agentic Systems"
Mozannar et al. arXiv 2025. [paper]
- "Self-Improving World Modelling with Latent Actions"
Qiu et al. arXiv 2026. [paper] - "Mastering Diverse Control Tasks through World Models"
Hafner et al. Nature 2025. [paper] - "Recurrent World Models Facilitate Policy Evolution"
Ha et al. NeurIPS 2018. [paper]
- "Learning to (Learn at Test Time): RNNs with Expressive Hidden States"
Sun et al. ICML 2025. [paper] - "Continual Learning via Sparse Memory Finetuning"
Lin et al. arXiv 2025. [paper]
- "Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents"
Cai et al. arXiv 2026. [paper] - "SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation"
Kim et al. arXiv 2026. [paper] - "VERDICT: Verifiable Evolving Reasoning with Directive-Informed Collegial Teams for Legal Judgment Prediction"
Liao et al. arXiv 2026. [paper] - "Memory Intelligence Agent"
Qiao et al. arXiv 2026. [paper] - "SLEA-RL: Step-Level Experience Augmented Reinforcement Learning for Multi-Turn Agentic Training"
Wang et al. arXiv 2026. [paper] - "SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning"
Xia et al. arXiv 2026. [paper] - "Self-Consolidation for Self-Evolving Agents"
Yu et al. arXiv 2026. [paper] - "MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents"
Zhang et al. arXiv 2026. [paper] - "RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback"
Zhang et al. arXiv 2026. [paper] - "EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle"
Wu et al. arXiv 2025. [paper] - "AgentEvolver: Towards Efficient Self-Evolving Agent System"
Zhai et al. arXiv 2025. [paper] - "Memento: Fine-tuning LLM Agents without Fine-tuning LLMs"
Zhou et al. arXiv 2025. [paper]
- "S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Research"
Team arXiv 2026. [paper] - "PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor"
Yang et al. arXiv 2026. [paper] - "Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration"
Zhang et al. arXiv 2026. [paper] - "EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks"
Feng et al. arXiv 2025. [paper]
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