AtomMem is a long‑term memory system for large language models that stores high‑value atomic facts extracted from long‑form interactions. It organizes these facts into hierarchical event structures and temporal profiles, enabling coherent episodic context and dynamic tracking of user attributes. During retrieval, an associative memory graph connects fragmented memories, and experiments on the LoCoMo benchmark show state‑of‑the‑art performance across reasoning tasks.
By Yanyu Yao, Shangze Li, Zhi Zheng, Hui Zheng, Qi Liu, Tong Xu, Enhong Chen
arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.
By Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, Yunjun Gao
arXiv:2607. 16211v1 Announce Type: new Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment.
By Zicheng Zhao, Xinyang Guo, Luyao Lv, Menghan Wang, Ming Li, Shuaicheng Li
arXiv:2607. 04617v1 Announce Type: new Abstract: Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window.
By Jizhizi Li, Amy Shi-Nash
Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window. An agent must preserve useful prior experience, retrieve it selectively, distinguish personal context from external evidence, and revise memory when the underlying situation changes.
The paper "Beyond Static Summarization: Proactive Memory Extraction for LLM Agents" identifies two shortcomings in current memory extraction for large language model agents: (1) extraction occurs ahead of time and mixes multiple types of information, leading to loss of useful details, and (2) extraction is typically one‑off, allowing errors and hallucinations to persist. To address these issues, the authors propose ProMem, a proactive framework that separates details, events, and relations, applies distinct extraction strategies for each, checks for completeness, and verifies facts at an atomic level. Experiments demonstrate that ProMem enhances memory completeness and question‑answering accuracy while maintaining a favorable balance between quality and token cost.
By Chengyuan Yang, Zequn Sun, Wei Wei, Wei Hu
arXiv:2607. 25992v1 Announce Type: cross Abstract: Recently, memory management has become a key infrastructure for LLM-based agents, as it directly affects long-horizon reasoning, personalized responses, and knowledge reuse.
By Shuyue Wei, Chang Liu, Zimu Zhou, Yongxin Tong, Lizhen Cui
arXiv:2605.28009v2 Announce Type: replace-cross
Abstract: Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. Ho...
By Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian, Jiayu Liu, William M. Campbell, Yue Wu, Yuji Zhang, Kathleen McKeown, Dilek Hakkani-Tur, Heng Ji
DyadMem introduces a new benchmark for evaluating how long‑term agents remember user‑specific relational information, called User‑conditioned Relational Agent Memory (URAM). The dataset contains 3,065 episodes, 50,961 sessions, and 61,210 QA pairs, with detailed annotations for memory capture, update, and recall across multi‑session trajectories. Experiments on 20 models show strong performance in a gold‑memory setting but a significant drop in full‑pipeline QA, highlighting gaps in current LLM memory capabilities.
By Yifei Tao, Xinyu Zhong, Henry Hengyuan Zhao, Fanyi Wang, Tengda Guo, Wentao Qiu, Ying Wang, Liujian Tang
The paper investigates how large language model agents can over-rely on agentic memory, a phenomenon where retrieved memories distort inference even when they are correctly stored and retrieved. It shows that memory is helpful when past experience fully transfers to the current task but becomes misleading under partial query-memory overlap, a pattern confirmed by controlled experiments. To address this, the authors propose MEMTRIM, a plug‑and‑play framework that indexes memory evidence at write time and limits its reuse at read time, thereby reducing over-reliance without retraining and preserving useful memory benefits across models and memory architectures.
By Luoxi Tang, Yuqiao Meng, Nilesh Auradkar, Muchao Ye, Dazheng Zhang, Zhaohan Xi
arXiv:2606. 29778v1 Announce Type: cross Abstract: Long-term conversational agents need to remember and query cross-session, multi-typed information with complex correlations.
By Yuhan Zhang (Institute of Software, Chinese Academy of Sciences), Zhiyuan Guo (Institute of Software, Chinese Academy of Sciences), Ziheng Zeng (Institute of Software, Chinese Academy of Sciences), Wei Wang (Institute of Software, Chinese Academy of Sciences), Wentao Wu (Microsoft Research), Lijie Xu (Institute of Software, Chinese Academy of Sciences)
arXiv:2603. 00026v2 Announce Type: replace-cross Abstract: Memory management is essential for LLM agents in long-term interactions.
By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yaqin Jin, Yazhong Zhang, Wei Hu