Author

Yin Xu

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Preprint Sep 2026

RiVaT-Fuse: Reliability-Calibrated Variational Tensor Fusion with Matrix-Valued Trust for Multimodal Prediction under Modality Uncertainty

Multimodal prediction from images and structured metadata requires integrating complementary evidence whose reliability can vary across samples and latent directions. A single confidence weight per modality cannot capture this directional variation. We propose RiVaT-Fuse, a reliability-calibrated variational tensor fus...

Yin Xu, Tie-Ming Liu, Ye Liang · 2 citations

#artificial intelligence Review Sep 2026

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

This survey traces the evolution from LM-driven SemCom to TokenCom, and examines the token abstraction, the transmission techniques it requires, and two emerging paradigms, namely TokenCom for LM services and for embodied and agentic intelligence.

Yu Ma, Zhen Gao, Li Qiao et al. · 0 citations

#machine learning Preprint Sep 2026

RiVaT-Fuse: Reliability-Calibrated Variational Tensor Fusion for Multimodal Prediction under Modality Uncertainty

RiVaT-Fuse is proposed, a reliability-calibrated variational tensor fusion framework that defines fusion as sample-wise latent-state estimation and achieves the strongest overall predictive rank among direct representation-level baselines while improving probability and label stability under perturbation.

Yin Xu, Tie-Ming Liu, Ye Liang et al. · 1 citation