8 citations · 16 across the 8 of their papers we have counts for
7 papers · 1 filter
Token-Efficient Multi-Agent Collaboration via System One-Guided Computational Division of Labor
Zihan Zhou, Xinzhe Hu, Hanxu Yang +2
Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by enabling collaborative problem so…
Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
Hanxu Yang, Yuhuan Zhao, Xiaodong He +1
Pre-training Graph Neural Networks (GNNs) via self-supervised learning has become a dominant paradigm, yet efficiently adapting frozen encoders remains a challenge. Graph prompting…
Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
Xiangyu Wang, Shuo Wang, Ruiyi Fang +1
Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters. As routing becomes increasin…
SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models
Xiaodong He, Xincheng Wang, Zhao Kang
Graph foundation models (GFMs) aim to learn transferable representations across severely heterogeneous graph domains. However, severe domain shifts in topology, graph scale, and fe…
Dependency, Compression, and Synergy: A Unified Information-Theoretic View of Multimodal Learning
Liangjian Wen, Linjie Li, Jiang Duan +3
Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement information. Mutual Information (MI…
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Shuo Wang, Xiangyu Wang, Quanxin Wang +9
Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlyi…
Structure-Centric Graph Foundation Model via Geometric Bases
Xiaodong He, Haolan He, Ruiyi Fang +2
Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible node feature spaces. We propose S…