COPE (Continual Optimization with Personalized embedding and self-Evaluation) is a new framework that continually personalizes large language models using learnable user embeddings and self‑evaluation to generate proxy rewards. It integrates preference capture, self‑evaluation calibration, and personalized response optimization into a single update step, allowing continuous model updates even when explicit user feedback is sparse. Experiments demonstrate that COPE outperforms both training‑free and training‑based baselines, remains complementary to Retrieval‑Augmented Prompting, and shows reliable self‑evaluation, meaningful preference patterns, stable general capabilities, and robustness to shifting preferences and alternative evaluators.
By Ruike Cao, Fugen Yao, Liang Dong, Jian Xu, Guanjun Jiang, Li Xiao
arXiv:2601. 09974v2 Announce Type: replace Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time.
By Seoyeon Kim, Jaehyung Kim
HyperTrace is a training‑free framework that personalizes large language models by tracing latent user preferences online. It maintains interpretable natural‑language hypotheses about short‑term intent and long‑term preferences, updating them with an SMC‑style reweighting process driven by an LLM‑based surrogate choice model. Experiments on PRISM and PersonaMem‑v2 demonstrate that HyperTrace improves response alignment, preference prediction, and profile consistency compared to strong online baselines.
By Jianzhi Shen, Keyu Mao, Minghao Shao, Chuanyang Jin, Yusong Wang, Ailiang Lin, Kotaro Funakoshi, Manabu Okumura, Tianmin Shu, Muhammad Shafique
arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.
By Yuchen Ma, Yue Huang, Wenjie Wang, Xiaonan Luo, Xiangliang Zhang, Stefan Feuerriegel
arXiv:2603.04191v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
By Qianyun Guo, Yibo Li, Yue Liu, Bryan Hooi
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:2609.37800v1 Announce Type: cross
Abstract: Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two cha...
By Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal, Ilias Ek\c{s}i, Rahul Sharma, Julia Mueller, Theresa Dombrowski, Jakob Karolus, Viktor Bengs, Eyke H\"ullermeier, Sebastian Vollmer
arXiv:2608.28833v1 Announce Type: new
Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providin...
By Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, Ge Wang
The paper introduces TAP-PER, a prefix‑based framework that learns compact user representations for large language model personalization. By encoding user preferences into lightweight prefix embeddings and incorporating temporal signals, TAP‑PER avoids the need for heavy per‑user adapters or prompt‑serialized histories. Experiments on six LaMP tasks show that TAP‑PER outperforms both prompt‑based and model‑based baselines while using far fewer per‑user parameters, enabling scalable personalization at large user scales.
By Heng Cao, Fan Zhang, Jian Yao, Yujie Zheng, Changlin Zhao, Lu Hao, Yuxuan Wei, Wangze Ni, Huaiyu Fu, Yuqian Sun, Xuyan Mo
The paper introduces ReaLMem, a benchmark built from authentic multi‑year personal visual archives with first‑person annotations, designed to evaluate AI systems on factual recall, persona inference, and predictive personalization. It also proposes ChronoProfiler, a temporal‑weighting module that calculates stability scores for user attributes to resolve preference conflicts and enhance personalized decision making. Experiments with multimodal large language models and memory systems show that predictive personalization remains the hardest task, highlight performance gaps, and demonstrate that temporally informed representations significantly improve personalization.
By Wenqi Zhou, Zhuorui Yu, Kaiao Wen, Hao Zheng, Xinyi Zheng, Peiran Wu, Enmin Zhou, Chi-Hao Wu, Junxiao Shen
arXiv:2609.15598v1 Announce Type: cross
Abstract: Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evol...
By Xinyu Lin, Zhuosong Jiang, Zixiao Suo, Siqin Wang, Hanqing Zeng, Hanchao Yu, Yinglong Xia, Jiang Zhang, Aashu Singh, Fei Liu, Wenjie Wang, Fuli Feng, Yang Song, Qifan Wang, Tat-Seng Chua
The paper introduces the Multi-Session Personalized Tool Calling (MPT) benchmark, containing 4,695 instances across 459 multi‑session histories that test Preference Recall, Induction, and Transfer. It proposes PRefine, a test‑time memory method that refines a user’s latent preference via a generate‑verify‑refine loop. Experiments with five LLMs show that PRefine outperforms existing memory systems and even full‑history prompting on Preference Transfer, suggesting that personalized agents should encode behavior as preferences rather than merely storing past interactions.
By Yejin Yoon, Minseo Kim, Taeuk Kim