2 citations · 6 across the 16 of their papers we have counts for
20 papers
SEEDS: Preview-Guided Evidence Admission for Search Agents
Jiajin Liu, Chenxi Xu, Dongzhe Fan +2
Search agents extend large reasoning models with multi-round access to external knowledge, yet most existing systems largely conflate retrieving evidence with consuming it. Retriev…
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…
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…
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)…
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…
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Jiajin Liu, Dongzhe Fan, Jiacheng Shen +3
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality a…
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…
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs
Yi Fang, Bowen Jin, Jiacheng Shen +3
The rapid development of Multimodal Large Language Models (MLLMs) has enabled the integration of multiple modalities, including texts and images, within the large language model (L…
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…
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
Yi Fang, Dongzhe Fan, Daochen Zha +1
This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods t…
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
Yi Fang, Dongzhe Fan, Sirui Ding +2
Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommend…
E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification
Xin Zhang, Daochen Zha, Qiaoyu Tan
This work studies ensemble learning for graph neural networks (GNNs) under the popular semi-supervised setting. Ensemble learning has shown superiority in improving the accuracy an…
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Qiaoyu Tan +4
Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly fr…