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To address this challenge, we propose SOTAlign, a two-stage framework that first recovers a coarse shared geometry from limited paired data using a linear teacher, and then refines the alignment on unpaired samples via an optimal-transport-based divergence that transfers relational structure without ","title":"SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport","url":"https://arxiv.org/abs/2602.23353","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.23353v2 Announce Type: replace-cross \nAbstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits this convergence by aligning frozen pretrained vision and language models with lightweight alignment layers, but typically relies on contrastive losses and millions of paired samples. 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