14 citations · 32 across the 38 of their papers we have counts for
43 papers
Personal-Agent Mediated Recommendation with Cross-Platform User History
Yu Xia, Jiangfan Zhang, Jun Xiao +2
Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the user's behalf across service…
Agent2UCB: Agentic System for Generative Engine Optimization
Sheldon Yu, Rui Wang, Tong Yu +4
Large language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization (GEO) the practice of refini…
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Sheldon Yu, Tong Yu, Xunyi Jiang +6
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shap…
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
Rohan Surana, Gagan Mundada, Junda Wu +9
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models…
FERA: Uncertainty-Aware Federated Reasoning for Large Language Models
Ruhan Wang, Chengkai Huang, Zhiyong Wang +6
Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across organizations that cannot c…
Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
Zihan Huang, Junda Wu, Tong Yu +6
While LLM-based agents excel at planning and executing long action sequences, their execution often remains inconsistent across trials, limiting reliability. Consolidating agent co…
Learning to Hint for Reinforcement Learning
Yu Xia, Canwen Xu, Zhewei Yao +2
Group Relative Policy Optimization (GRPO) is widely used for reinforcement learning with verifiable rewards, but it often suffers from advantage collapse: when all rollouts in a gr…
WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning
Gagan Mundada, Zihan Huang, Rohan Surana +8
Group Relative Policy Optimization (GRPO) is effective for training language models on complex reasoning. However, since the objective is defined relative to a group of sampled tra…
AMPS: Adaptive Modality Preference Steering via Functional Entropy
Zihan Huang, Xintong Li, Rohan Surana +5
Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may ove…
Evaluation on Entity Matching in Recommender Systems
Zihan Huang, Rohan Surana, Zhouhang Xie +3
Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack…
SceneAlign: Aligning Multimodal Reasoning to Scene Graphs in Complex Visual Scenes
Chuhan Wang, Xintong Li, Jennifer Yuntong Zhang +5
Multimodal large language models often struggle with faithful reasoning in complex visual scenes, where intricate entities and relations require precise visual grounding at each st…
The 2nd Workshop on Human-Centered Recommender Systems
Kaike Zhang, Jiakai Tang, Du Su +6
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Hongtao Huang, Chengkai Huang, Junda Wu +3
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…
Importance Sampling for Multi-Negative Multimodal Direct Preference Optimization
Xintong Li, Chuhan Wang, Junda Wu +4
Direct Preference Optimization (DPO) has recently been extended from text-only models to vision-language models. However, existing methods rely on oversimplified pairwise compariso…
Pluralistic Off-policy Evaluation and Alignment
Chengkai Huang, Junda Wu, Zhouhang Xie +6
Personalized preference alignment for LLMs with diverse human preferences requires evaluation and alignment methods that capture pluralism. Most existing preference alignment datas…
MusiCRS: Benchmarking Audio-Centric Conversational Recommendation
Rohan Surana, Amit Namburi, Gagan Mundada +4
Conversational recommendation has advanced rapidly with large language models (LLMs), yet music remains a uniquely challenging domain in which effective recommendations require rea…
Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
Sheldon Yu, Yuxin Xiong, Junda Wu +6
Recent advances in chain-of-thought (CoT) prompting have enabled large language models (LLMs) to perform multi-step reasoning. However, the explainability of such reasoning remains…
SAND: Boosting LLM Agents with Self-Taught Action Deliberation
Yu Xia, Yiran Shen, Junda Wu +5
Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these…
DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer
Ruoyu Wang, Junda Wu, Yu Xia +4
Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks. However, existing works ha…
PDB-Eval: An Evaluation of Large Multimodal Models for Description and Explanation of Personalized Driving Behavior
Junda Wu, Jessica Echterhoff, Kyungtae Han +3
Understanding a driver's behavior and intentions is important for potential risk assessment and early accident prevention. Safety and driver assistance systems can be tailored to i…
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition
Junda Wu, Yuxin Xiong, Xintong Li +7
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to break down complex problems into interpretable intermediate steps, significantly enhancing model transparen…
Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation
Ruoyu Wang, Tong Yu, Junda Wu +3
Visual Language Navigation (VLN) is a fundamental task within the field of Embodied AI, focusing on the ability of agents to navigate complex environments based on natural language…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Zhouhang Xie, Junda Wu, Yiran Shen +9
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the a…