On the quantitative analysis of decoder-based generative models

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

arXiv:2605.29713v2 Announce Type: replace-cross Abstract: This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence....

Transformer-based Encoder-Decoder Models

FFJORD: Free-form continuous dynamics for scalable reversible generative models

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

arXiv:2506. 00849v2 Announce Type: replace Abstract: Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically underexplored, especially lacking a full consideration of the shared encoder-generator structure.

Generative Models: Principles, Architectures, and Applications

arXiv:2608. 08101v1 Announce Type: new Abstract: Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content.

Bridging Reconstruction and Generation: A Latent Distribution Perspective on Evaluation and Improvement

The paper investigates why reconstruction quality in latent generative models does not always predict generative performance, attributing the issue to a mismatch between encoder-induced and generation-time latent distributions. It introduces Generation‑Aware Reconstruction (GAR), a method that perturbs encoder latents with noise and denoises them through the generative model before decoding, creating a continuous trajectory that reveals how the decoder behaves across latent spaces. The resulting GAR‑FID metric correlates strongly with generation FID, and using intermediate GAR latents for decoder adaptation consistently improves generative quality across different model scales.