New Primer Maps the Math Behind Generative AI Models
A new book posted to the arXiv preprint server aims to explain the mathematical foundations of generative artificial intelligence. Written by Tianhua Chen and first submitted May 28, 2026, the compact text skips a survey of recent architectures and instead follows the ideas that link major model families. Those include PCA, variational autoencoders, diffusion models, normalizing flows, GANs, Wasserstein GANs and energy-based models. The author describes it as a foundation-building primer for mathematically curious researchers, practitioners and students. A revised version followed on September 8, 2026. Read more

















