activity
20242026
most citedA Survey of Personalization: From RAG to Agent

2 citations · 3 across the 8 of their papers we have counts for

collaborators

14 papers

cs.IR2026

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…

cs.CL2026

Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation

Yimin Deng, Yuqing Fu, Derong Xu +10

Conversational agents struggle to handle long conversations due to context window limitations. Therefore, memory systems are developed to leverage essential historical information.…

cs.IR2026

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…

cs.AI2025

Efficient Reasoning via Reward Model

Yuhao Wang, Xiaopeng Li, Cheng Gong +4

Reinforcement learning with verifiable rewards (RLVR) has been shown to enhance the reasoning capabilities of large language models (LLMs), enabling the development of large reason…

cs.IR20251 cited

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…

cs.CL2025

Align-GRAG: Anchor and Rationale Guided Dual Alignment for Graph Retrieval-Augmented Generation

Derong Xu, Pengyue Jia, Xiaopeng Li +9

Despite the strong abilities, large language models (LLMs) still suffer from hallucinations and reliance on outdated knowledge, raising concerns in knowledge-intensive tasks. Graph…