Continual Learning for LLM Agents

LLM: Large Language Model

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23 papers in the last four weeks, up 109% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 123

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AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models
May 27, 2026Ruiyi Zhang, Peijia Qin, Qi Cao +2Continual Learning for LLM Agents AI Agents for Scientific Discovery
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA · Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft
May 26, 2026Yuchen Guo, Junli Gong, Weicheng Wang +3Continual Learning for LLM Agents LLM Agent Memory
Northwestern University · Northeastern University · South China University of Technology +2
Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models
May 26, 2026Joan Vendrell Gallart, Russell Bent, Michael GrosskopfContinual Learning for LLM Agents Language Model-Based Control
Department of Mechanical and Aerospace University of California Irvine Irvine, CA 92617-4322, USA · T-5 Los Alamos National Laboratory Los Alamos, NM 88220, USA · CAI-4 Los Alamos National Laboratory Los Alamos, NM 88220, USA
Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents
May 21, 2026Xing Zhang, Yanwei Cui, Guanghui Wang +4Continual Learning for LLM Agents LLM Agent Self-Improvement
AWS Generative AI Innovation Center · HSBC Holdings Plc., HSBC Technology Center, China
Echo: Learning from Experience Data via User-Driven Refinement
May 21, 2026Hande Dong, Xiaoyun Liang, Jiarui Yu +15Continual Learning for LLM Agents LLM Agent Self-Improvement
Tencent, CodeBuddy
Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents
May 20, 2026Chongrui Ye, Yuxiang Liu, Yu Wang +5Continual Learning for LLM Agents Continual Learning
University of Illinois Urbana-Champaign · University of California San Diego
Training Language Agents to Learn from Experience
May 19, 2026Yuval Shalev, Zifeng Ding, Mateja JamnikContinual Learning for LLM Agents Self-Improving Agents
University of Cambridge
Library Drift: Diagnosing and Fixing a Silent Failure Mode in Self-Evolving LLM Skill Libraries
May 19, 2026Xing Zhang, Yanwei Cui, Guanghui Wang +4Continual Learning for LLM Agents LLM Agent Self-Improvement
1AWS Generative AI Innovation Center · 2HSBC Holdings Plc., HSBC Technology Center, China.
MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer
May 17, 2026Xuefei Wang, Jialu Wang, Fengbo Zhang +6Continual Learning for LLM Agents Multi-Agent LLM Systems
Beihang University · Independent Researcher · Beijing University of Posts and Telecommunications
Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
May 14, 2026Han Li, Jinyu Tian, Rili Feng +10RL for Code Generation Continual Learning for LLM Agents
Nanjing University · Independent Researcher · Tsinghua University
LiSA: Lifelong Safety Adaptation via Conservative Policy Induction
May 14, 2026Minbeom Kim, Lesly Miculicich, Bhavana Dalvi Mishra +6Continual Learning for LLM Agents LLM Guardrails
Google Cloud AI Research · Google · Seoul National University
MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs
May 11, 2026Ruiyi Yang, Zechen Li, Hao Xue +2Agent Memory Continual Learning for LLM Agents
University of New South Wales · The Hong Kong University of Science and Technology (Guangzhou) · Mohamed Bin Zayed University of Artificial Intelligence
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
May 11, 2026Seth Karten, Joel Zhang, Tersoo Upaa +5Continual Learning for LLM Agents Game-Playing Agents
Princeton University · ARISE Foundation · Google DeepMind
NEXUS: Continual Learning of Symbolic Constraints for Safe and Robust Embodied Planning
May 10, 2026Tiehan Cui, Peipei Liu, Yanxu Mao +3Continual Learning for LLM Agents Large Language Model-Based Robot Planning
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China · School of Software, Henan University, China · Institute of Information Engineering, Chinese Academy of Sciences, China +2
Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
May 10, 2026Ye Yu, Xiaopeng Yuan, Haibo Jin +3Continual Learning for LLM Agents LLM Agent Self-Improvement
University of Illinois Urbana-Champaign, IL, USA
Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution
May 9, 2026Feng Xiong, Zengbin Wang, Yong Wang +5Continual Learning for LLM Agents Agent Skill Learning
1AMAP, Alibaba Group · 2CASIA · 3BNU
SkillOS: Learning Skill Curation for Self-Evolving Agents
May 7, 2026Siru Ouyang, Jun Yan, Yanfei Chen +13Continual Learning for LLM Agents LLM Agent Skill Learning
University of Illinois Urbana-Champaign · Google Cloud AI Research · Massachusetts Institute of Technology
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
May 7, 2026Jinghao Luo, Yuchen Tian, Chuxue Cao +6Continual Learning for LLM Agents LLM Agent Memory
2South China Normal University · 1Hong Kong Baptist University · 3Hong Kong University of Science and Technology +2
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment
May 5, 2026Siyuan Guo, Yali Du, Hechang Chen +2Continual Learning for LLM Agents Continual Learning for LLMs
aSchool of Artificial Intelligence, Jilin University. · bEngineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University. · cInternational Center of Future Science, Jilin University. +3
Contextual Agentic Memory is a Memo, Not True Memory
Apr 30, 2026Binyan Xu, Xilin Dai, Kehuan ZhangAgent Memory Continual Learning for LLM Agents
The Chinese University of Hong Kong, Hong Kong, China · Zhejiang University, Hangzhou, China
Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
Apr 29, 2026Bochao Liu, Zhipeng Qian, Yang Zhao +10Continual Learning for LLM Agents LLM Agent Orchestration
Kuaishou Technology
When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
Apr 29, 2026Qisheng Hu, Quanyu Long, Wenya WangContinual Learning for LLM Agents LLM Agent Memory
Nanyang Technological University
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
Apr 28, 2026Xirui Liu, Sihang Zhou, Yanning Hou +6Continual Learning for LLM Agents LLM Agent Skill Learning
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China · College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
Apr 22, 2026Xiyang Wu, Zongxia Li, Guangyao Shi +5Continual Learning for LLM Agents Game-Playing Agents
University of Maryland · University of Southern California · Good Start Labs +2
SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks
Apr 22, 2026Shanshan Zhong, Yi Lu, Jingjie Ning +7Continual Learning for LLM Agents LLM Agent Evaluation
Carnegie Mellon University · Amazon AGI
APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay
Mar 31, 2026Pratyay Banerjee, Masud Moshtaghi, Ankit ChadhaContinual Learning for LLM Agents Persistent Memory for Language Models
Amazon, AGI / Sunnyvale, USA
Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
Jan 26, 2026Yibo Li, Zijie Lin, Ailin Deng +5Continual Learning for LLM Agents Reinforcement Learning
National University of Singapore, Singapore.
A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents
Jan 18, 2026Tianxin Wei, Ting-Wei Li, Zhining Liu +26Continual Learning for LLM Agents Multi-Agent Reasoning
University of Illinois Urbana-Champaign · Meta · UCSD +2
Evolving Programmatic Skill Networks
Jan 7, 2026Haochen Shi, Xingdi Yuan, Bang LiuContinual Learning for LLM Agents LLM-Based Program Synthesis
DIRO & Institut Courtois, Université de Montréal · Mila – Québec AI Institute · Microsoft Research +1
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Feb 3, 2025Minttu Alakuijala, Ya Gao, Georgy Ananov +4Continual Learning for LLM Agents LLM Agent Skill Learning
Department of Computer Science, Aalto University · Department of Computer Science, University of Manchester · System 2 AI

May 27, 2026, cs.AIJ/K move · Enter open · S save

AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models

Ruiyi Zhang, Peijia Qin, Qi Cao, Li Zhang+1

Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA · Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA

Continual Learning for LLM Agents AI Agents for Scientific Discovery Knowledge Augmentation for Language Models Automated Machine Learning

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AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual, requiring practitioners to design architectures, build training pipelines, and iteratively refine solutions, making it challenging for natural scientists without specialized AI engineering expertise to build the high-performing models their research demands. To reduce this burden and broaden access to AI for scientific discovery, agents that automatically build AI models have been proposed. However, the performance of these agents is largely limited by the parametric knowledge of their underlying large language models, which is static, often outdated, and sparse on practical AI model engineering know-how. To address this limitation, we introduce AIBuildAI-2, a knowledge-enhanced agent with an external, evolving knowledge system for automatically building AI models. The knowledge system of AIBuildAI-2 is hierarchical, organizing curated AI development knowledge into high-level knowledge instructions over topical categories and low-level knowledge documents under each category, from which the agent dynamically loads only the context relevant to its current state and the AI task being solved, grounding each design and implementation decision in concrete, externally verifiable expertise. The system is initialized by collecting and cleaning AI-development-related documents from the web and organizing them into the corresponding categories, and continually evolves from the agent's own experience by distilling each completed run on an AI task into structured takeaways that are written back into the knowledge system. AIBuildAI-2 achieves state-of-the-art results, ranking first on MLE-Bench with a 70.7% medal rate and placing in the top 6.6% among 4,370 human-expert teams in a heart disease prediction competition.

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