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arXiv · 2605.05646

MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality

Panqi Yang·Haodong Jing·Jiahao Chao·Tingyan Xiang·Li Lin·Yao Hu·Yang Luo·Yongqiang Ma
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Abstract

Unified visual tokenization faces a fundamental trade-off between high-fidelity pixel reconstruction (spatial equivariance) and semantic abstraction (conceptual invariance). We attribute this conflict to Manifold Misalignment: naive joint optimization induces opposing gradients, creating a zero-sum game between reconstruction and perception. To address this, we propose MUSE, a framework based on Topological Orthogonality. By treating Structure as an orthogonal bridge, MUSE decouples optimization within Transformers: structural gradients refine attention topology, while semantic gradients update feature values. This turns destructive interference into Mutual Reinforcement. Experiments show that MUSE breaks the trade-off, achieving state-of-the-art generation quality (gFID 3.08) and surpassing its teacher InternViT-300M in linear probing (85.2\% vs. 82.5\%), demonstrating that structurally aligned reconstruction can enhance semantic perception. Code is available at https://github.com/PanqiYang1/MUSE.

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Panqi Yang, Haodong Jing, Jiahao Chao, Tingyan Xiang, Li Lin, Yao Hu, Yang Luo, Yongqiang Ma. 2026-05-07. MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality. https://arxiv.org/abs/2605.05646

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