1 citations · 1 across the 7 of their papers we have counts for
9 papers
GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning
Jiayi Yang, Yifang Chen, Yuanfu Sun +2
LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph learning remains unexplored. Gr…
DAGent: Evaluate-then-Grow Planning for Deep Research Agents
Hanwen Liu, Yuanfu Sun, Qiaoyu Tan
Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emerge. Directed acyclic graph (DA…
GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models
Yuanfu Sun, Yuanhang Ren, Kang Li +5
Recent Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse vision-language tasks, creating an urgent need for more challenging benchmarks. Yet…
One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
Jiayi Yang, Yifang Chen, Yuanfu Sun +2
Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data…
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…