arXiv AI August 18, 2026 By Heng Wang, Yifei Li, Lingling Zhang, Pengyu Li, Xinyu Che, Xinyu Zhang, Zesheng Yang

QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents

arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 2

Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory

arXiv:2607. 00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user.

By ZhiShu Jiang, Haibo Liu, Xin Shen, Guanqiang QI, Chenxi Miao, Weikang Li, Liwei Qian, Xin Pei, Jizhou Huang

llms agents benchmarks

arXiv AI

Aug 5

LeanMem: Simple and Efficient Long-Term Memory for LLM Agents

arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.

By Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang

llms agents nlp

arXiv AI

Jun 16

T-Mem: Memory That Anticipates, Not Archives

arXiv:2606. 15405v1 Announce Type: cross Abstract: Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user.

By Weidong Guo, Dakai Wang, Zixuan Wang, Hui Liu, Yu Xu

llms agents benchmarks

arXiv Machine Learning

Aug 4

LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference

arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.

By Zhichen Liu, Ruihan Sun, Hengjie Yang, Zipeng Wu, Zhaohan Chen, Xiaofan Zhang, Yang Xu

llms agents nlp

arXiv AI

Jun 12

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.

By Minjae Kim, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang

llms agents benchmarks

arXiv AI

Jun 3

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents

arXiv:2510. 16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states.

By Ao Tian, Yunfeng Lu, Xinxin Fan, Changhao Wang, Lanzhi Zhou, Yeyao Zhang, Yanfang Liu

llms rag agents nlp benchmarks

Related stories

Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory

LeanMem: Simple and Efficient Long-Term Memory for LLM Agents

T-Mem: Memory That Anticipates, Not Archives

LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents