NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation
Authors: name: Huichao Zhang; name: Liao Qu; name: Yiheng Liu; name: Hang Chen; name: Yangyang Song; name: Yongsheng Dong; name: Shikun Sun; name: Xian Li; name: Xu Wang; name: Yi Jiang; name: Hu Ye; name: Bo Chen; name: Yiming Gao; name: Peng Liu; name: Akide Liu; name: Zhipeng Yang; name: Qili Deng; name: Linjie Xing; name: Jiyang Liu; name: Zhao Wang; name: Yang Zhou; name: Mingcong Liu; name: Yi Zhang; name: Qian He; name: Xiwei Hu; name: Zhongqi Qi; name: Jie Shao; name: Zhiye Fu; name: Shuai Wang; name: Fangmin Chen; name: Xuezhi Chai; name: Zhihua Wu; name: Yitong Wang; name: Zehuan Yuan; name: Daniel K. Du; name: Xinglong Wu
Published: 2026-01-05 15:27:04

Abstract: We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.