1.6k citations · 1.6k across the 11 of their papers we have counts for
12 papers
Information-Gain Calibrated On-Policy Distillation for Deep Search Agents
Chenxi Li, Xueyang Feng, Songyang Liu +4
On-policy distillation (OPD) has recently emerged as a promising approach for transferring the capabilities of large agents to compact students. In deep search, where agents iterat…
Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
Yuanzi Li, Quanyu Dai, Xueyang Feng +5
Conversational Recommender Systems (CRSs) enhance user experience through multi-turn interactions, yet evaluating their performance remains challenging. While Large Language Model…
How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants
Xueyang Feng, Weinan Gan, Xu Chen +2
Large language model (LLM)-powered assistants have recently integrated memory mechanisms that record user preferences, leading to more personalized and user-aligned responses. Howe…
Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi +12
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
Luyu Chen, Quanyu Dai, Zeyu Zhang +6
Conversational recommender systems (CRS) enhance user experience through multi-turn interactions, yet evaluating CRS remains challenging. User simulators can provide comprehensive…
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Xueyang Feng, Jingsen Zhang, Jiakai Tang +6
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generat…
Explainable Recommendation with Simulated Human Feedback
Jiakai Tang, Jingsen Zhang, Zihang Tian +3
Recent advancements in explainable recommendation have greatly bolstered user experience by elucidating the decision-making rationale. However, the existing methods actually fail t…
Improving Retrospective Language Agents via Joint Policy Gradient Optimization
Xueyang Feng, Bo Lan, Quanyu Dai +5
In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents o…
Enhancing Recommendation Explanations through User-Centric Refinement
Jingsen Zhang, Zihang Tian, Xueyang Feng +1
Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground…
Large Language Model-based Human-Agent Collaboration for Complex Task Solving
Xueyang Feng, Zhi-Yuan Chen, Yujia Qin +4
In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite…
A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Ma, Xueyang Feng +10
Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited…
Fairness-aware Cross-Domain Recommendation
Jiakai Tang, Xu Chen, Xueyang Feng
Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping funct…