9 papers
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…
MLaGA: Multimodal Large Language and Graph Assistant
Dongzhe Fan, Yi Fang, Jiajin Liu +2
Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis. Prevailing LLM-based graph methods excel in adapting LLMs to text-r…
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…
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
Haofu Yang, Jiaji Liu, Chen Huang +3
Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large langua…
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)…