The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

Tianhua Chen has released a revised 195-page preprint, “The Little Book of Generative AI Foundations,” on arXiv (2605.29713), adding visual explanations throughout the derivation-oriented primer. The book traces a single mathematical path through generative modeling, covering PCA and probabilistic PCA, variational autoencoders, diffusion models, normalizing flows, autoregressive factorizations, GANs and Wasserstein GANs, and energy-based models rather than surveying recent architectures. It is aimed at mathematically curious researchers, practitioners, and students who want the derivations behind how these model families relate, not implementation detail. The revision matters because accessible, unified teaching material on generative-model math remains scarce even as such models dominate AI research and commercial deployment. Watch for further corrections and adopters using the text as a course or onboarding reference.