The workshop welcomes submissions across a range of themes designed to capture emerging trends and persistent challenges in generative modeling. These tracks serve as organizing principles, not strict categories, and we encourage creative, boundary-pushing contributions.

Novel architectures   This track highlights recent advances in model design that expand the expressive capacity, (data-)efficiency, or adaptability of generative models. We’re particularly interested in approaches that depart from established paradigms or reimagine the structure of generative systems in meaningful ways.

Novel training paradigms   Training remains one of the most delicate aspects of generative modeling. This track focuses on methods that propose alternative learning strategies—whether by stabilizing objectives, improving sample efficiency, or addressing challenges like convergence and mode collapse.

Evaluation-driven innovations   Progress in generative modeling is tightly coupled with how we define and measure success. This track invites work where insights from evaluation—whether metric-based, human-centered, or task-specific—lead to deeper understanding or new modeling techniques.

Application-driven innovations   Generative models are increasingly deployed in settings with complex constraints and requirements. This track focuses on work where the demands of a specific application inspire new methods, formulations, or modeling considerations.

Why does this not work?   Failure is often more instructive than success. This track provides space for thoughtful analysis of approaches that fall short—whether due to limitations in theory, implementation, or assumptions.