Firehose

This paper investigates how Diffusion Language Models (DLMs) internally represent time and how this representation can be used to modulate the model's behavior. Practitioners might care because understanding how DLMs process time could lead to more controllable and interpretable models.

This paper proposes a new way to improve the performance of diffusion models by aligning their inference process with the forward statistical structure of the model. This can lead to more realistic and diverse generated images, which is important for applications like image generation.