arXiv AI

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.

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

arXiv AI

Jul 8

From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space

arXiv:2607. 05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence.

By Yue Xu, Yutao Sun, Yihao Liu, Mengyu Zhou, Jiayi Qiao, Lu Ma, Kai Tang, Wenjie Wang, Xiaoxi Jiang, Guanjun Jiang

agents reinforcement-learning

arXiv AI

Jun 15

VikingMem: A Memory Base Management System for Stateful LLM-based Applications

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

llms agents benchmarks

arXiv AI

Jul 31

Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data

arXiv:2607. 27056v1 Announce Type: new Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks.

By Lingyang Zeng, Guangze Chen, Kaichen Yu, Zhicheng Pan, Siyang Weng, Zirui Hu, Xiangyun Du, Hailin He, Rong Zhang, Chengcheng Yang, Kai Huang, Xuan Zhou

llms agents benchmarks safety

arXiv AI

Jun 9

MemToolAgent overview with a simple restaurant booking scenario where the agent retrieves similar memories, receives feedback on an invalid time format, and generates a reflection to update its memory

arXiv:2606. 07909v1 Announce Type: new Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.

By Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar, Surafel Lakew, Adi Kalyanpur, James Gung, Thomas Delteil, Arshit Gupta

llms agents fine-tuning benchmarks

arXiv AI

Jul 24

RUMBA: Russian User Memory Benchmark

arXiv:2607. 21447v1 Announce Type: cross Abstract: The ability to handle long-term memory in LLMs is becoming increasingly critical, yet existing benchmarks remain English-centric and rely on aggregate retrieval metrics, failing to capture interactions between long-range context, temporal information, and reasoning.

By Elizaveta Shevtsova, Inna Glebkina, Mark Baushenko, Pavel Gulyaev, Alena Fenogenova

llms benchmarks

arXiv AI

Jul 15

MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations

arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.

By Xixuan Hao, Zeyu Zhang, Zehao Lin, Yihang Sun, Ziliang Guo, Xichong Zhang, Yuxuan Liang, Feiyu Xiong, Zhiyu Li

llms agents nlp benchmarks

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