AI RESEARCH
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
arXiv CS.LG
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ArXi:2605.17232v1 Announce Type: new Discrete diffusion has become a leading framework for generative modeling in various applications including language, vision, and biology. Existing convergence theory, however, exhibits fundamental limitations. KL-based analyses diverge under singular priors such as the masked distribution, while bounds in total variation (TV) depend on the state space size $S$ and become vacuous for modern language tasks, where vocabularies contain hundreds of thousands of tokens.