OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit Flows

ICLR 2026 Conference Submission18815 Authors

Keywords: multimodal, flow matching

TL;DR: diffusion-based multimodal model for variable-length, concurrent, interleaved text-image generation

Abstract: 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 a insertion-based Edit Flows for discrete text tokens and 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.

Primary Area: generative models

Submission Number: 18815