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
arXiv:2606. 06054v1 Announce Type: new Abstract: Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions.
By Jiawen Zhang, Kejia Chen, Jiachen Ma, Yangfan Hu, Lipeng He, Yechao Zhang, Jian Liu, Xiaohu Yang, Tianwei Zhang, Ruoxi Jia
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.
By Lingxiang Xu, Jiaoyun Yang, Min Hu, Hongtu Chen, Ning An
The paper introduces AIM, a privacy‑aware memory framework that lets multi‑agent, multi‑user large language models manage both private and shared memory. AIM classifies data as private (user‑specific) or public (shared) and enforces index‑level access controls to protect sensitive information while enabling shared knowledge to improve coordination. The authors also present MUMBench, a new dataset for evaluating memory operations in multi‑user settings, and report high accuracy metrics for AIM on this benchmark.
By Zachary Johnson, Nigel Boachie Kumankumah, Somya Chatterjee, Tejas Sathyamurthi, Min Chen, Xinyi Alice Li, Xiao Wang, Emily Morgan Gelchie, Jessica Lin, Sadid A. Hasan, Sulaiman Vesal
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
The paper introduces MemPABench, an interactive environment designed to evaluate how personal agents remember and act on user interaction preferences. The benchmark features long‑horizon tasks where agents must decide when to clarify, confirm, or act autonomously, using data from four simulated users who provided 99 sessions over four months and five preference changes each. Experiments across four memory systems and LLM backbones show that current memory approaches offer limited improvement in preference selection, with retrieval‑based methods performing no better than no memory and file‑based memory sometimes applying preferences in irrelevant contexts.
By Jialin Li, Zhenhao Chen, Hanjun Luo, Hanan Salam