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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.02204 |
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| _version_ | 1866912802637086720 |
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| author | Zhang, Huichao Qu, Liao Liu, Yiheng Chen, Hang Song, Yangyang Dong, Yongsheng Sun, Shikun Li, Xian Wang, Xu Jiang, Yi Ye, Hu Chen, Bo Gao, Yiming Liu, Peng Liu, Akide Yang, Zhipeng Deng, Qili Xing, Linjie Liu, Jiyang Wang, Zhao Zhou, Yang Liu, Mingcong Zhang, Yi He, Qian Hu, Xiwei Qi, Zhongqi Shao, Jie Fu, Zhiye Wang, Shuai Chen, Fangmin Chai, Xuezhi Wu, Zhihua Wang, Yitong Yuan, Zehuan Du, Daniel K. Wu, Xinglong |
| author_facet | Zhang, Huichao Qu, Liao Liu, Yiheng Chen, Hang Song, Yangyang Dong, Yongsheng Sun, Shikun Li, Xian Wang, Xu Jiang, Yi Ye, Hu Chen, Bo Gao, Yiming Liu, Peng Liu, Akide Yang, Zhipeng Deng, Qili Xing, Linjie Liu, Jiyang Wang, Zhao Zhou, Yang Liu, Mingcong Zhang, Yi He, Qian Hu, Xiwei Qi, Zhongqi Shao, Jie Fu, Zhiye Wang, Shuai Chen, Fangmin Chai, Xuezhi Wu, Zhihua Wang, Yitong Yuan, Zehuan Du, Daniel K. Wu, Xinglong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02204 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation Zhang, Huichao Qu, Liao Liu, Yiheng Chen, Hang Song, Yangyang Dong, Yongsheng Sun, Shikun Li, Xian Wang, Xu Jiang, Yi Ye, Hu Chen, Bo Gao, Yiming Liu, Peng Liu, Akide Yang, Zhipeng Deng, Qili Xing, Linjie Liu, Jiyang Wang, Zhao Zhou, Yang Liu, Mingcong Zhang, Yi He, Qian Hu, Xiwei Qi, Zhongqi Shao, Jie Fu, Zhiye Wang, Shuai Chen, Fangmin Chai, Xuezhi Wu, Zhihua Wang, Yitong Yuan, Zehuan Du, Daniel K. Wu, Xinglong Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.02204 |