collaborators

7 papers

cs.CV2026

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…

cs.LG2026

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…

cs.CL2026

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…

cs.CV2026

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)…

cs.CL2026

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…

cs.LG2025

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…