2 citations · 3 across the 8 of their papers we have counts for
10 papers · 1 filter
To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention
Wenlin Zhang, Kuicai Dong, Junyi Li +9
Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current agents…
Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
Wenlin Zhang, Xiangyang Li, Qiyuan Ge +9
In recommender systems, online A/B testing is a crucial method for evaluating the performance of different models. However, conducting online A/B testing often presents significant…
Deep Research: A Survey of Autonomous Research Agents
Wenlin Zhang, Xiaopeng Li, Yingyi Zhang +5
The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks. Despite their impressive capab…
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Wenlin Zhang, Xiangyang Li, Kuicai Dong +9
Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information. However…
A Survey of Personalization: From RAG to Agent
Xiaopeng Li, Pengyue Jia, Derong Xu +11
Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent r…
Joint Modeling in Recommendations: A Survey
Xiangyu Zhao, Yichao Wang, Bo Chen +7
In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional meth…