Memory Management for LLM Agents

LLM: Large Language Model

Latest papers

110 papers51 papers in the last four weeks, up 538% on the four weeks before. 0.3% of all new papers.

Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Jun 5, 2026Hao Yang, Shiqi Shen, Haoxuan Li +3LLM Agent Memory Persistent Memory for Language Models
Gaoling School of Artificial Intelligence, Renmin University of China · Weixin, Tencent · Institute for Artificial Intelligence, Peking University
MemTrain: Self-Supervised Context Memory Training
Jun 2, 2026Ziheng Li, Xingrun Xing, Haoqing Wang +2LLM Agent Memory Persistent Memory for Language Models
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Samsung Research, Beijing, China
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
May 29, 2026Tao Feng, Chongrui Ye, Tianyang Luo +5LLM Agent Memory Persistent Memory for Language Models
University of Illinois Urbana-Champaign · Nanyang Technological University
RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents
May 15, 2026Zijie Dai, Shiyuan Deng, Sheng Guan +4Long-Horizon LLM Agents Agent Memory Management
Department of Computer Science and Engineering, The Chinese University of Hong Kong · Huawei Cloud · School of Computer Science, Beijing University of Posts and Telecommunications +2
DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory
May 15, 2026Wentao Qiu, Haotian Hu, Fanyi Wang +2LLM Agent Memory Persistent Memory for Language Models
1StepOS · 2Xiamen University · 3ShanghaiTech University
Useful Memories Become Faulty When Continuously Updated by LLMs
May 13, 2026Dylan Zhang, Yanshan Lin, Zhengkun Wu +4AI Agent Reliability Persistent Memory for Language Models
University of Illinois Urbana-Champaign · IIIS, Tsinghua University
MEME: Multi-entity & Evolving Memory Evaluation
May 12, 2026Seokwon Jung, Alexander Rubinstein, Arnas Uselis +2Agent Memory Persistent Memory for Language Models
1KAIST AI · 2Tübingen AI Center, University of Tübingen · 3NAVER AI Lab
Human-Inspired Memory Architecture for LLM Agents
May 8, 2026Doga Kerestecioglu, Alexei Robsky, Clemens Vasters +2Agent Memory Management LLM Agent Memory
Microsoft
MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents
May 5, 2026Jiayi Chen, Yingcong Li, Guiling WangLLM Agent Workflow Optimization Persistent Memory for Language Models
New Jersey Institute of Technology, Newark, USA
Lightweight LLM Agent Memory with Small Language Models
Apr 9, 2026Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9Memory Management for LLM Agents Persistent Memory for Language Models
University of Electronic Science and Technology of China · Kyung Hee University · City University of Hong Kong +1
MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents
Apr 6, 2026Shu Wang, Edwin Yu, Oscar Love +4LLM Agent Memory Memory Management for LLM Agents
MemVerge, Inc.
LightThinker++: From Reasoning Compression to Memory Management
Apr 4, 2026Yuqi Zhu, Jintian Zhang, Zhenjie Wan +7Memory Management for LLM Agents Efficient Language Model Reasoning
Zhejiang University · Ant Group
Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory
Mar 4, 2026Zhenting Wang, Huancheng Chen, Jiayun Wang +1LLM Agent Memory Memory Management for LLM Agents
Center for Advanced AI, Accenture
Towards Autonomous Memory Agents
Feb 25, 2026Xinle Wu, Rui Zhang, Mustafa Anis Hussain +1Memory-Augmented Language Model Agents Memory Management for LLM Agents
National University of Singapore, Singapore
Pancake: Hierarchical Memory System for Multi-Agent LLM Serving
Feb 25, 2026Zhengding Hu, Zaifeng Pan, Prabhleen Kaur +6Memory Management for LLM Agents Agent Memory Management
Computer Science and Engineering, University of California, San Diego
AMV-L: Lifecycle-Managed Agent Memory for Tail-Latency Control in Long-Running LLM Systems
Feb 22, 2026Emmanuel BamideleMemory Management for LLM Agents Agent Memory Management
Department of Computer Science, Georgia Institute of Technology
Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices
Feb 17, 2026Yakov Pyotr ShkolnikovMemory Management for LLM Agents LLM Agent Memory
MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
Feb 2, 2026Haozhen Zhang, Quanyu Long, Jianzhu Bao +4Memory Management for LLM Agents Memory Consolidation for LLM Agents
Nanyang Technological University · University of Illinois Urbana-Champaign · University of Illinois Chicago +1
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Jan 8, 2026Chengyuan Yang, Zequn Sun, Wei Wei +1LLM Agent Memory Persistent Memory for Language Models
State Key Laboratory for Novel Software Technology, Nanjing University, China · National Institute of Healthcare Data Science, Nanjing University, China
HiMA-Ecom: A Long-Horizon Memory Benchmark for Hierarchical Multi-Agent E-commerce Assistants
Jun 24, 2025Junxing Hu, Ai Han, Haolan Zhan +8Hierarchical MARL Long-Horizon Agent Evaluation
JD Retail, JD.com, Inc., Beijing, 101111, China · Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, Victoria, 3082, Australia · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

Jun 5, 2026J/K move · Enter open · S save

Rosetta Memory: Adaptive Memory for Cross-LLM Agents

Hao Yang, Shiqi Shen, Haoxuan Li, Zhipeng Wang+2

Gaoling School of Artificial Intelligence, Renmin University of China · Weixin, Tencent · Institute for Artificial Intelligence, Peking University

LLM Agent Memory Persistent Memory for Language Models Agent Memory Management Memory-Augmented Language Model Agents Memory Management for LLM Agents

Open paper AskPDF arXiv

Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvement. Existing memory systems typically take the LLM as the center and design memory operations tailored to a specific backbone. In practice, however, users frequently switch between LLMs, for example using Claude for coding and GPT for writing across tasks, or routing different steps to different backbones within a single task for cost-effective trade-offs. As a result, memory written by one model often needs to be consumed by another. Making upstream memory effectively adapt to and activate downstream LLMs remains a critical yet underexplored problem. To bridge this gap, we shift the perspective from LLM-centric memory design to memory-centric LLM adaptation. Specifically, we approach the above upstream-downstream memory adaptation problem from both the write and read sides, and design two profile-conditioned operators that are jointly trained to optimize how memory is stored and presented for better task completion. To ensure the learned operators generalize across a broad set of LLMs, we propose a minimum-gain sampling curriculum that prioritizes the least-served LLMs during training. To better measure the operators' actual contribution rather than the LLM's own capability, we design a performance-gap reward that compares against a naive memory baseline. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue demonstrate that our model consistently outperforms baselines and remains robust under unseen-model replacement.