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This paper analyzes the embedding space of Masked Diffusion Language Models (MDLMs) and finds it is hyperspherical, making linear interpolation suboptimal. The authors introduce Spherical Soft-Masking (S-SM), which uses SLERP and Fréchet means on the hypersphere, improving MAUVE and perplexity over prior soft-masking approaches.