Memory-Augmented Language Model Agents

Follow this topic Ask about this topic

Momentum

50 papers in the last four weeks, up 233% on the four weeks before. 0.5% of all new papers.

Jul 13Week of Sep 28

Latest papers 275

All topics

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents
Jan 7, 2026Dehao Tao, Guoliang Ma, Yongfeng Huang +1LLM Memory Long-Term Conversational Memory
Tsinghua University · Xinjiang University
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
Jan 14, 2025Shengsheng Zhou, Shuai Wang, Liang Ding +5Code Generation Agentic Code Generation
College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China · The University of Sydney, Sydney, Australia +3
PACEvolve: Enabling Progress-Aware Consistent Evolution
Date pendingMinghao Yan, Bo Peng, Benjamin Coleman +13Evolutionary Optimization Large Language Model-Guided Optimization
University of Wisconsin–Madison · Google · Google DeepMind +1
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
Date pendingRakibul Hasan Rajib, Mengxin Zheng, Qian LouMulti-Agent LLM Systems Multi-Agent System Optimization
University of Central Florida
SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation
Date pendingQi Liu, Qinzheng Wang, Can Li +2Continual Learning for LLM Agents LLM Agent Self-Improvement
School of Transportation, Jilin University, 5988 Renmin St., Changchun, Jilin 130022, China · Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation, Tongji University, 4800 Cao’an Rd., Shanghai 201804, China

Jan 7, 2026, cs.CLJ/K move · Enter open · S save

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

Dehao Tao, Guoliang Ma, Yongfeng Huang, Minghu Jiang

Tsinghua University · Xinjiang University

LLM Memory Long-Term Conversational Memory Agent Memory Management Memory-Augmented Language Model Agents

Open paper AskPDF arXiv

Long-term human-agent dialogues are organized by topic continuity: adjacent turns often develop the same goal, plan, problem, or event, while related activities may recur across distant sessions. Yet many LLM agent memory systems first decompose histories into isolated turns or fixed-size chunks, then compensate through enrichment, consolidation, or retrieval mechanisms still tied to semantic proximity or fragment-level records. This weakens temporal and causal organization and biases memory access toward semantic proximity rather than task- or topic-level continuity. We introduce \emph{Membox}, a hierarchical memory architecture that instantiates topic continuity as an explicit organization layer for agent memory. Its \textbf{Topic Loom} incrementally organizes dialogue streams into boxes whose internal turns follow the same local topic, while its \textbf{Trace Weaver} links extracted events across boxes into macro-topic traces that recover recurring activities, goals, and factual developments across distant sessions. On LoCoMo, Topic-Loom-only retrieval improves over the best Mem0/A-MEM retrieval-depth setting by 13.00 F1 points (53.95 vs. 40.95), and trace-expanded retrieval further raises F1 to 55.28; with GPT-4o, trace-expanded retrieval reaches 59.71 F1. Additional DialSim results show the same gain from adding cross-box traces in multi-party dialogue. These results show that local topic-continuity organization and macro-topic trace expansion improve long-range memory beyond semantic retrieval over fragmented records.

Momentum

Related topics

Latest papers 275

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models

PACEvolve: Enabling Progress-Aware Consistent Evolution

Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems

SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation