Foundations of Deep Generative Models

Diffusion models, flow-based models, and autoregressive models have delivered impressive empirical gains. At the same time, major open questions remain around reliability, interpretability, privacy, and scientific use. This workshop creates a focused venue for theory, empirical analysis, and domain-driven applications to meet.

The program is designed around a practical scientific question: when a model appears capable, is it reproducing training data, capturing a real distributional structure, or performing a stronger form of compositional reasoning that transfers beyond what it has seen?

This workshop aims to bring together researchers working on the foundations of diffusion models, flow-based models, autoregressive models, and related generative learning frameworks. We welcome work that sharpens our understanding of what DGMs learn, how they behave under scale, and how they can be evaluated reliably.

The topics below reflect the research directions prioritized for the workshop and are intended to guide the scope of submissions and discussion during the workshop.