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: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
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
By Garry Yang, Zizhe Chen, Xinru Chen, Yongqiang Chen, Jianxiang Wang, Deyu Zou, Linyi Ding, Jialiang Wu, Yunzhong He, Yu Gong, James Cheng, Huaixiao Tou
The paper introduces RAVEL, a retrieval‑aware online reinforcement learning framework designed to improve interactive retrieval under partial evidence. RAVEL begins with supervised question generation, directly observes the top‑4 retrieval candidates, and refines its question policy using rank feedback from the full question‑answer‑retrieval loop. Experiments on the Interactive‑PEDES dataset demonstrate that RAVEL progressively enhances retrieval performance over five interaction rounds, reallocating questioning toward localized open‑ended attributes that yield the greatest gains on challenging queries.
By Lyucheng Qian, John Yuehan Zhang, Pingyu Wang
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
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.