1 citations · 1 across the 5 of their papers we have counts for
5 papers
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
Yuanfu Sun, Kang Li, Dongzhe Fan +2
Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Ye…
Mario: Multimodal Graph Reasoning with Large Language Models
Yuanfu Sun, Kang Li, Pengkang Guo +2
Recent advances in large language models (LLMs) have opened new avenues for multimodal reasoning. Yet, most existing methods still rely on pretrained vision-language models (VLMs)…
GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning
Jiajin Liu, Yuanfu Sun, Dongzhe Fan +1
Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability…
TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks
Qihai Zhang, Xinyue Sheng, Yuanfu Sun +1
Inspired by the success of large language models (LLMs), there is a significant research shift from traditional graph learning methods to LLM-based graph frameworks, formally known…
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
Yuanfu Sun, Zhengnan Ma, Yi Fang +2
The growing importance of textual and relational systems has driven interest in enhancing large language models (LLMs) for graph-structured data, particularly Text-Attributed Graph…