Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion

ArXi:2604.13470v1 Announce Type: new We prove that conditional diffusion models whose reverse kernels are finite Gaussian mixtures with ReLU-network logits can approximate suitably regular target distributions arbitrarily well in context-averaged conditional KL divergence, up to an irreducible terminal mismatch that typically vanishes with increasing diffusion horizon.