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Review Sep 2026

Dependency, Compression, and Synergy: A Unified Information-Theoretic View of Multimodal Learning

Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement information. Mutual Information (MI), the Information Bottleneck (IB), and Partial Information Decomposition (PID) provide complementary perspectives, yet existing studies oft...

Liang-Jian Wen, Lin Li, Jiang Duan et al. · 1 citation
#artificial intelligence Preprint Sep 2026

SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models

Graph foundation models (GFMs) aim to learn transferable representations across severely heterogeneous graph domains. However, severe domain shifts in topology, graph scale, and feature semantics impede the construction of a unified, domain-agnostic representation space. To address this, we propose SCGFM-ART, a structu...

Xiao-Dong He, Xin-Cheng Wang, Zhao Kang · 1 citation

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