As LLM agents increasingly rely on persistent memory for long-horizon and personalized behavior, they can retain and reuse information across interactions, but this also creates a lasting channel thro...
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
arXiv:2606. 22030v2 Announce Type: replace Abstract: We investigate when belief-based memory actually improves large language model (LLM) agents.
By Pranav Singh
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:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
By Ben Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang
AdaMem introduces adaptive memory policies that allow personalized agents to decide what information to write into long‑term memory based on user preferences for each interaction context. Each policy is updated from periodic feedback and controls subsequent memory writing, aiming to improve relevance and reduce unnecessary memory persistence. In experiments on AdaMem‑Bench, AdaMem raises QA accuracy from 80.0% to 84.35% while cutting persistent memory by 9.27%, though models still struggle to execute policies reliably.
By Xingyu Chen, Rui Wang, Zhaopeng Tu, Liefeng Bo