The paper introduces REPAIR, a method that corrects lossy user preference states in personalization encoders by comparing cached representations with the current state in a learned coordinate space. REPAIR selectively aggregates corrective evidence from past interactions and adds it to the state before the task head, enabling encoder–host repair without re‑encoding history. Experiments on MovieLens, PENS, MIND, and Amazon Reviews 2023 show that training only REPAIR improves MRR and nDCG@10 across all twelve recommendation hosts, while head‑only finetuning yields smaller gains.
By Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan, Sourish Dasgupta, Manjunath Joshi, Tanmoy Chakraborty
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.
By Heng Wang, Yifei Li, Lingling Zhang, Pengyu Li, Xinyu Che, Xinyu Zhang, Zesheng Yang
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
ReMem is a new recommendation agent framework that rethinks perception and memory for long-context recommendation tasks. It replaces raw HTML parsing with OCR-based multimodal perception from screenshots, extracting structured information in a platform-agnostic way. The framework also introduces a chunk-wise sequential memory update strategy and a multi-memory GRPO variant to efficiently model evolving user preferences over arbitrarily long interaction histories, achieving a 5.16% average improvement over state-of-the-art baselines on three recommendation agent tasks.
By Haohao Qu, Yongcheng Jing, Chun Hin Chan, Shanru Lin, Wenqi Fan, Dacheng Tao
arXiv:2606. 09803v1 Announce Type: cross Abstract: We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models.
By Wayne King, Zeyue Xue, Yuxuan Bian, Jie Huang, Haoran Li, Yaowei Li, Yaofeng Su, Yuming Li, Haoyu Wang, Shiyi Zhang, Songchun Zhang, Yuwei Niu, Sihan Xu, Junhao Zhuang, Haoyang Huang, Nan Duan
arXiv:2608. 12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions.
By Xuefei Wang, Jun Han, Zixuan Wang, Qingkai Zeng, Xiao Wang, Ruijie Wang, Jianxin Li
arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.
By Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo
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
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
arXiv:2609.36435v1 Announce Type: new
Abstract: An assistant that serves the same user over a long horizon has to answer from what that user has revealed: which preferences still hold, which were rev...
By Jingxuan Wu, Yuzhe Yang, Yiqiao Huang, Chengzhi Liu, Qingni Wang, Chengxuan Qian, Shutong Wu, Jiawei Zhang, Xin Eric Wang
arXiv:2608. 12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length.
By Ming Zhang, Kaisen Yang, Shu Yu, Ermo Hua, Ning Ding, Xia Hu, Bowen Zhou, Chaochao Lu, Youbang Sun
arXiv:2607. 24804v2 Announce Type: replace-cross Abstract: Recommendation systems have undergone significant transformations in the past years.
By David Bauer, Cancan Zhang, Wenshun Liu, Xiaoyi Zhang, Weijia Liu, Wanli Ma, Yue Weng, Wei Li, Rui Li, Jing Qian, Huayu Li, Xiaoyi Liu, Linhong Zhu, Jerry Fu