Confidence Limits in Diffusion Models

This post introduces a theoretical framework for understanding the limits of confidence in discrete diffusion models. It demonstrates that current sampling methods often violate conditional independence assumptions, leading to significant deviations from the training distribution. The work proposes a new understanding of how to measure and potentially improve confidence in diffusion model outputs.