Representation by Design in Generation: Cross-View Class-Token Alignment in Diffusion Transformers

Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas the models' own representations are often treated as a by-product of synthesis. We ask whether diffusion models can instead be trained to learn substanti