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20242026
most citedPersonalized Image Generation with Large Multimodal Models

7 citations · 8 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2026

Towards Root Memory: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs

Hongxun Ding, Xiang Yu, Chengbing Wang +4

Memory systems are essential for personalized Large Language Models (LLMs). However, retrieval within these systems primarily relies on semantic similarity, potentially missing log…

cs.CL2026

From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users

Wuqiang Zheng, Chengbing Wang, Yilin Yang +6

As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research…

cs.IR2026

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges

Bohao Wang, Yu Cui, Zhenxiang Xu +13

The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…

cs.CL2026

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models

Chengbing Wang, Wuqiang Zheng, Yang Zhang +5

Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL…

cs.MA2026

FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting

Xiangyu Li, Xuan Yao, Guohao Qi +16

Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and l…

cs.CL2025

Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation

Chengbing Wang, Yang Zhang, Wenjie Wang +4

Preference alignment has enabled large language models (LLMs) to better reflect human expectations, but current methods mostly optimize for population-level preferences, overlookin…

cs.IR2025★ 1 cited

NextQuill: Causal Preference Modeling for Enhancing LLM Personalization

Xiaoyan Zhao, Juntao You, Yang Zhang +5

Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…

cs.CV2025

DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition

Yiyan Xu, Wuqiang Zheng, Wenjie Wang +5

Personalized image generation has emerged as a promising direction in multimodal content creation. It aims to synthesize images tailored to individual style preferences (e.g., colo…

cs.IR2025

Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation

Yi Fang, Wenjie Wang, Yang Zhang +4

Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these a…

cs.IR2024★ 7 cited

Personalized Image Generation with Large Multimodal Models

Yiyan Xu, Wenjie Wang, Yang Zhang +4

Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing c…