OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit
  Flows
Authors: name: John Nguyen, name: Marton Havasi, name: Tariq Berrada, name: Luke Zettlemoyer, name: Ricky T. Q. Chen
Organization: AI at Meta

We present OneFlow, the first non-autoregressive multimodal model that
enables variable-length and concurrent mixed-modal generation. Unlike
autoregressive models that enforce rigid causal ordering between text and image
generation, OneFlow combines an insertion-based Edit Flow for discrete text
tokens with Flow Matching for image latents. OneFlow enables concurrent
text-image synthesis with hierarchical sampling that prioritizes content over
grammar. Through controlled experiments across model sizes from 1B to 8B, we
demonstrate that OneFlow outperforms autoregressive baselines on both
generation and understanding tasks while using up to 50% fewer training FLOPs.
OneFlow surpasses both autoregressive and diffusion-based approaches while
unlocking new capabilities for concurrent generation, iterative refinement, and
natural reasoning-like generation.