One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion.

arXiv:2609.33698v1 Announce Type: new Abstract: Most continuous diffusion language models process one latent position per token at each sampling step, making generation expensive. Two-stage methods lower the cost by reducing the latent length, but they fix the