Multimodal Co-Generation With Continuous Bitstream Diffusion
We show that continuous bitstream diffusion (CoBit), originally developed for language modeling, provides a unified framework for joint and conditional multimodal generation using the same training and sampling protocol. Across image-text, speech-text and protein sequence-structure modeling, models are trained separately from scratch, but each uses a single input projection, transformer backbone, bitwise prediction head and binary score-matching objective shared across its modalities. Within each setting, a single checkpoint supports joint generation and the conditional directions included in training by changing which parts of the bitstream are observed. We first validate the approach on...
| Authors | Georgios Batzolis, Georgios Kontos, Bruno Trentini et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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