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Ming Cheng

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#natural language process... Preprint Sep 2026

Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalist

HWM is a representation-level framework that models each domain-task specialist as a distribution of hidden representations on a shared support, enabling either training-free specialist aggregation by Wasserstein-derived weights or training-based generalist learning through a hybrid Wasserstein alignment loss.

Ming Cheng, Jiaying Gong, Hoda Eldardiry · 0 citations

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