8 papers
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…
GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
Jiajin Liu, Dongzhe Fan, Chuanhao Ji +2
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where…
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…