4 citations · 5 across the 8 of their papers we have counts for
19 papers
DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
Yusheng Zhao, Jiaye Xie, Qixin Zhang +5
Graph neural networks (GNNs) have been widely used in various graph machine learning scenarios. Existing literature primarily assumes well-annotated training graphs, while the reli…
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks…
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
Yangning Li, Weizhi Zhang, Yuyao Yang +17
Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inf…
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang +10
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further adva…
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Weizhi Zhang, Yangning Li, Yuanchen Bei +20
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engi…
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +10
Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach i…