[2603.20645] Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity

Computer Science > Machine Learning arXiv:2603.20645 (cs) [Submitted on 21 Mar 2026] Title:Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity Authors:Zixuan Zhang, Kaixuan Huang, Tuo Zhao, Mengdi Wang, Minshuo Chen View a PDF of the paper titled Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity, by Zixuan Zhang and 4 other authors View PDF Abstract:Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated on low-dimensional structures -- remains incomplete. This paper investigates how diffusion models learn such structured data, focusing on two key aspects: statistical complexity and influence of data geometric properties. By modeling data as samples from a smooth Riemannian manifold, our analysis reveals crucial decompositions of score functions in diffusion models under different levels of injected noise. We also highlight the interplay of manifold curvature with the structures in the score function. These analyses enable an efficient neural network approximation to the score function, built upon which we further provide statistical rates for score estimation and distribution learning. Remarkably, the obtained statistical rates are governed by the intrinsic dimension of data and the manifold curvature. These results advance the statistical foundations of diffusion models, bridging th...