HC-DLM makes a continuous latent the only persistent generative state and beats both discrete and continuous diffusion baselines on Sudoku, Countdown and LM1B
The work proposes Hierarchical Continuous Diffusion Language Models (HC-DLM), in which a continuous latent is the only state persisting across steps while tokens are read out from it at every step and fed back as a scaffold conditioning the next latent update, with a training objective derived from a variational bound on the token likelihood; at matched model size it improves over discrete and continuous diffusion baselines in puzzle accuracy on Sudoku and Countdown and in generative perplexity on LM1B.