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A Theory of Speciation in Generative Diffusion Models on Compact Riemannian Manifolds
Speciation in generative diffusion models denotes the emergence of distinct stable branches during denoising, through which initially undifferentiated trajectories progressively commit to different data classes. In this work we develop an intrinsic theory of speciation for diffusion models supported on compact Riemannian manifolds: the aim is to go beyond existing theoretical descriptions, which usually identify speciation with a symmetric pitchfork bifurcation and assume to work in a large-dimensional space. We characterize speciation by bifurcations of the critical points of the evolving pro
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-24T20:03:31.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.