13 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…
Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
Dongzhe Fan, Zheyi Xue, Siyuan Liu +1
Retrieval-augmented generation (RAG) and its graph-based extensions (GraphRAG) are effective paradigms for improving large language model (LLM) reasoning by grounding generation in…
GraphRAG-Router: Learning Cost-Efficient Routing over GraphRAGs and LLMs with Reinforcement Learning
Dongzhe Fan, Chuanhao Ji, Zimu Wang +2
Graph-based retrieval-augmented generation (GraphRAG) has recently emerged as a powerful paradigm for knowledge-intensive question answering, especially for tasks that require stru…