Author

Xu Wang

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#machine learning May 2026

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective

Graph-Optimized Multimodal Alignment (GOMA), which lets a jointly trained model support single-modality and dual-attribute retrieval through a task-specific readout, achieves state-of-the-art performance on all 14 primary measures against 14 external methods.

Xu Wang, Xun-Kai Li, Yin-Lin Zhu et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models

Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires more than compressing each node into a fused Euclidean vector. The representation must preserve entity semantics, construct interaction state from graph neighborhoods, and...

Xun-Kai Li, Xu Wang, Yin-Lin Zhu et al. · 0 citations