2 citations · 3 across the 22 of their papers we have counts for
24 papers
Learning to Steer LLM Agents and Multi-Agent Systems
Leyang Shen, Yaxin Gong, Yang Zhang +7
Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration. However, these system…
Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective
Xiaoyan Zhao, Yujie Cai, Yang Zhang +2
Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to ground their outputs in extern…
PrefReward: Learning User Preference Matrix for Personalized Text Generation
Yue Wu, Chengbing Wang, Yimeng Bai +3
Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing person…
Intuition-Guided Latent Reasoning for LLM-Based Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender syst…
Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play
Leyang Shen, Yang Zhang, Xiaoyan Zhao +2
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated great potential in solving tasks with execution complexity, by distributing subtasks across cooperative…
Preference-Aware Rubric Learning for Personalized Evaluation
Yilun Qiu, Xiaoyan Zhao, Yang Zhang +7
As Large Language Models (LLMs) evolve from general-purpose assistants to user-centric agents, personalization has become central to aligning model behavior with individual prefere…
Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology
Tianhao Shi, Yang Zhang, Xiaoyan Zhao +8
The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its beh…
Distribution-Level Contrastive Supervision for Generative Recommendation
Ziqi Xue, Dingxian Wang, Yimeng Bai +7
Recent generative recommenders improve scalability by retrieving items through token generation instead of traditional ranking over large candidate sets. Yet their training signals…
Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
Ziyi Zhao, Chongming Gao, Yang Zhang +5
Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts. However, these prompts become obsolete when the foundation model is upgraded, necessitat…
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…
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…
CARL: Criticality-Aware Agentic Reinforcement Learning
Leyang Shen, Yang Zhang, Chun Kai Ling +2
Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction. However, in such multi-step settings,…
Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization
Suyu Chen, Yimeng Bai, Yulong Huang +2
Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own…
SteerX: Disentangled Steering for LLM Personalization
Xiaoyan Zhao, Ming Yan, Yilun Qiu +5
Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life a…
Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
Yimeng Bai, Chang Liu, Yang Zhang +5
Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically invol…
Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts
Xiaoyan Zhao, Ming Yan, Yang Zhang +6
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations through multi-turn natural language interactions with users. Given the strong interaction and…
Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation
Chengbing Wang, Yang Zhang, Zhicheng Wang +4
Fine-tuning large language models (LLMs) for recommendation in a generative manner has delivered promising results, but encounters significant inference overhead due to autoregress…
Latent Inter-User Difference Modeling for LLM Personalization
Yilun Qiu, Tianhao Shi, Xiaoyan Zhao +3
Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs. Previous work focuses on leveraging a user's…
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation
Ziang Ye, Yang Zhang, Wentao Shi +3
Graphical User Interface (GUI) agents powered by Large Vision-Language Models (LVLMs) have emerged as a revolutionary approach to automating human-machine interactions, capable of…
Brownian Bridge Diffusion for Sequential Recommendation
Yimeng Bai, Yang Zhang, Sihao Ding +5
Diffusion models, known for their strong generative capability derived from iterative noising and denoising processes, have recently emerged as a promising paradigm for sequential…
DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +3
Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods…
IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation
Zijie Lin, Yang Zhang, Xiaoyan Zhao +3
Large Language Models (LLMs) have shown strong potential for recommendation by framing item prediction as a token-by-token language generation task. However, existing methods treat…
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…
Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems
Yimeng Bai, Shunyu Zhang, Yang Zhang +5
Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate abs…