arXiv AI

When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents

arXiv:2606. 06055v1 Announce Type: new Abstract: Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration into responses.

arXiv AI

Jun 4

PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

arXiv:2602. 01146v2 Announce Type: replace Abstract: Conversational assistants are increasingly integrating long-term memory with large language models (LLMs).

By Sidharth Pulipaka, Oliver Chen, Manas Sharma, Taaha S Bajwa, Vyas Raina, Ivaxi Sheth

llms benchmarks safety

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

Aug 11

Mitigating Over-Personalization in LLMs via Structured Memory

arXiv:2608. 08300v1 Announce Type: new Abstract: Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions.

By Hakeem Hannoon, Andrew Zhao, Mihir Narayan, Sharvin Goyal, Ivaxi Sheth

llms

arXiv AI

Aug 5

MemArena: An Ego-Centric Benchmark for On-Device Agentic Personal Memory Assistants at Scale

arXiv:2608. 02613v1 Announce Type: cross Abstract: Edge-deployed personal memory assistants must handle private interpersonal conversations on-device with open-weight models.

By Jiadong Zhang, Xiaosong Ma

rag agents benchmarks data-engineering

arXiv AI

Jul 24

CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents

arXiv:2607. 20458v1 Announce Type: cross Abstract: Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems either retain everything indiscriminately or apply uniform forgetting heuristics that fail to distinguish relevant from irrelevant knowledge.

By Haowen Lai

llms rag agents benchmarks

arXiv AI

Jun 30

Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

arXiv:2507. 05257v4 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks.

By Yuanzhe Hu, Yu Wang, Julian McAuley

llms rag agents benchmarks

arXiv AI

Jun 10

Deployment-Time Memorization in Foundation-Model Agents

arXiv:2606. 10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time function rather than solely a property of model weights.

By Lei (Rachel), Chen, Guilin Zhang, Kai Zhao, Dalmo Cirne, Andy Olsen, Xu Chu, Zeke Miller, Alet Blanken, Amine Anoun, Jerry Ting

llms agents nlp safety

arXiv AI

Aug 6

ContextWeave: A Real-World Workflow Benchmark

arXiv:2608. 04830v1 Announce Type: new Abstract: Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retrieval or question answering.

By Bo Wang, Yuqian Yao, Enxi Wang, Luozhijie Jin, Yang Liu, Yiran Suo, Yuxuan Cai, Enyu Zhou, Yufei Gao, Honglin Guo, Tianyu Huai, Li Ji, Zhikai Lei, Bufan Li, Lizhi Lin, Jinxiu Liu, Jie Yang, Jiazheng Zhou, Maosen Zhou, Pengfang Qian, Shichun Liu, Guanshan Liu, Hao Zheng, Yunhao Yu, Hang Yan, Jihua Kang, Xinchi Chen, Xipeng Qiu

agents nlp benchmarks safety

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

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

Hugging Face Trending Papers

Aug 5

ContextWeave: A Real-World Workflow Benchmark

Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retrieval or question answering. We introduce ContextWeave, a longitudinal benchmark that evaluates whether recalled experience improves downstream agent performance in realistic office-work streams.

agents nlp benchmarks safety

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

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