161 citations · 161 across the 5 of their papers we have counts for
6 papers
When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration
Yaxin Gong, Gangyi Zhang, Chongming Gao +7
Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful information or an incorrect answ…
Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning
Gangyi Zhang, Junjie Meng, Letian Zhang +6
Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressivel…
Sequence-aware Large Language Models for Explainable Recommendation
Gangyi Zhang, Runzhe Teng, Chongming Gao
Large Language Models (LLMs) have shown strong potential in generating natural language explanations for recommender systems. However, existing methods often overlook the sequentia…
Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning
Gangyi Zhang, Chongming Gao, Hang Pan +2
Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the…
Vague Preference Policy Learning for Conversational Recommendation
Gangyi Zhang, Chongming Gao, Wenqiang Lei +6
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit…
Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He +4
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user p…