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:2607. 22603v1 Announce Type: new Abstract: Personalized multimodal large language models (MLLMs) aim to generate user-specific responses, but existing methods mainly rely on profile-level information and overlook diverse user preferences.
By Fan Lyu, Wenqi Zhang, Joost van de Weijer
Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback.
arXiv:2607. 26473v1 Announce Type: new Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings.
By Haifeng Wu
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:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
By Lechen Zhang, Jiarui Liu, Tal August
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
arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.
By Harini SI, Somesh Singh, Yaman Kumar Singla, David Doermann, Rajiv Ratn Shah
Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs).
The paper introduces VIBE‑Bench, a new benchmark designed to test personalized large language models (PLLMs) in a regime where user profile cues and query‑specific preferences do not share the same conceptual space, a situation termed profile‑preference conceptual misalignment (PRCM). VIBE‑Bench contains 3,504 personas, 12,239 dialogues, and a manually verified gold test set, and includes two psychology‑grounded tasks that require cross‑concept preference reasoning beyond surface semantic overlap. Experiments show that existing PLLMs largely depend on shallow semantic correlations and struggle to learn robust cross‑concept mappings, highlighting PRCM as a distinct failure mode for personalization models.
By Yiwen Jiang, Yang Deng, Stephanie Fong, Zimu Wang, Yaling Shen, Wei Feng, Hongxi Yang, Xiangyu Zhao, Zhongxing Xu, Deval Mehta, Xuelian Cheng, Zongyuan Ge
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
PIVOTSBench is a benchmark designed to assess multimodal large language models’ ability to reason about fine‑grained interpersonal relationships. It is constructed from Social‑IQ 2.0 and YouTube data and evaluates models on predicting bidirectional relationship dimensions grounded in psychology research. The benchmark also includes auxiliary tasks that test models’ capacity to identify and use critical visual cues, and it examines the impact of visual modalities, social role information, and different prediction settings on model performance.
By Shuxiang Zhang, Yiting Yin, Wenxuan Song, Yuhang Wu, Miao Liu