Paper Detail
Meituan LongCat Team, Bin Xiao, Chao Wang, Chengjiang Li, Chi Zhang, Chong Peng, Hang Yu, Hao Yang, Haonan Yan, Haoze Sun, Haozhe Zhao, Hong Liu, Hui Su, Jiaqi Zhang, Jiawei Wang, Jing Li, Kefeng Zhang, Manyuan Zhang, Minhao Jing, Peng Pei, Quan Chen, Taofeng Xue, Tongxin Pan, Xiaotong Li, Xiaoyang Li, Xiaoyu Zhao, Xing Hu, Xinyang Lin, Xunliang Cai, Yan Bai, Yan Feng, Yanjie Li, Yao Qiu, Yerui Sun, Yifan Lu, Ying Luo, Yipeng Mei, Yitian Chen, Yuchen Xie, Yufang Liu, Yufei Chen, Yulei Qian, Yuqi Peng, Zhihang Yu, Zhixiong Han, Changran Wang, Chen Chen, Dian Zheng, Fengjiao Chen, Ge Yang, Haowei Guo, Haozhe Wang, Hongyu Li, Huicheng Jiang, Jiale Hong, Jialv Zou, Jiamu Li, Jianping Lin, Jiaxing Liu, Jie Yang, Jing Jin, Jun Kuang, Juncheng She, Kunming Luo, Kuofeng Gao, Lin Qiu, Linsen Guo, Mianqiu Huang, Qi Li, Qian Wang, Rumei Li, Siyu Ren, Wei Wang, Wenlong He, Xi Chen, Xiao Liu, Xiaoyu Li, Xu Huang, Xuanyu Zhu, Xuezhi Cao, Yaoming Zhu, Yifei Cao, Yimeng Jia, Yizhen Jiang, Yufei Gao, Zeyang Hu, Zhenlong Yuan, Zijian Zhang, Ziwen Wang
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal systems remain language-centric, often treating non-linguistic modalities as external attachments, leading to fragmented architectures and suboptimal integration. To transcend this limitation, we introduce Discrete Native Autoregressive (DiNA), a unified framework that represents multimodal information within a shared discrete space, enabling a consistent and principled autoregressive modeling across modalities. A key innovation is the Discrete Native Any-resolution Visual Transformer (dNaViT), which performs tokenization and de-tokenization at arbitrary resolutions, transforming continuous visual signals into hierarchical discrete tokens. Building on this foundation, we develop LongCat-Next, a native multimodal model that processes text, vision, and audio under a single autoregressive objective with minimal modality-specific design. As an industrial-strength foundation model, it excels at seeing, painting, and talking within a single framework, achieving strong performance across a wide range of multimodal benchmarks. In particular, LongCat-Next addresses the long-standing performance ceiling of discrete vision modeling on understanding tasks and provides a unified approach to effectively reconcile the conflict between understanding and generation. As an attempt toward native multimodality, we open-source the LongCat-Next and its tokenizers, hoping to foster further research and development in the community. GitHub: https://github.com/meituan-longcat/LongCat-Next
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@misc{team2026longcat,
title = {LongCat-Next: Lexicalizing Modalities as Discrete Tokens},
author = {Meituan LongCat Team and Bin Xiao and Chao Wang and Chengjiang Li and Chi Zhang and Chong Peng and Hang Yu and Hao Yang and Haonan Yan and Haoze Sun and Haozhe Zhao and Hong Liu and Hui Su and Jiaqi Zhang and Jiawei Wang and Jing Li and Kefeng Zhang and Manyuan Zhang and Minhao Jing and Peng Pei and Quan Chen and Taofeng Xue and Tongxin Pan and Xiaotong Li and Xiaoyang Li and Xiaoyu Zhao and Xing Hu and Xinyang Lin and Xunliang Cai and Yan Bai and Yan Feng and Yanjie Li and Yao Qiu and Yerui Sun and Yifan Lu and Ying Luo and Yipeng Mei and Yitian Chen and Yuchen Xie and Yufang Liu and Yufei Chen and Yulei Qian and Yuqi Peng and Zhihang Yu and Zhixiong Han and Changran Wang and Chen Chen and Dian Zheng and Fengjiao Chen and Ge Yang and Haowei Guo and Haozhe Wang and Hongyu Li and Huicheng Jiang and Jiale Hong and Jialv Zou and Jiamu Li and Jianping Lin and Jiaxing Liu and Jie Yang and Jing Jin and Jun Kuang and Juncheng She and Kunming Luo and Kuofeng Gao and Lin Qiu and Linsen Guo and Mianqiu Huang and Qi Li and Qian Wang and Rumei Li and Siyu Ren and Wei Wang and Wenlong He and Xi Chen and Xiao Liu and Xiaoyu Li and Xu Huang and Xuanyu Zhu and Xuezhi Cao and Yaoming Zhu and Yifei Cao and Yimeng Jia and Yizhen Jiang and Yufei Gao and Zeyang Hu and Zhenlong Yuan and Zijian Zhang and Ziwen Wang},
year = {2026},
abstract = {The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal systems remain language-centric, often treating non-linguistic modalities as external attachments, leading to fragmented architectures and suboptimal integration. To transcend this limitation, we introduce Discrete Native Autoregressive (DiNA), a unified framework that represents multimodal information within a shared discr},
url = {https://huggingface.co/papers/2603.27538},
keywords = {Next-Token Prediction, autoregressive modeling, multimodal systems, discrete space, Discrete Native Any-resolution Visual Transformer, LongCat-Next, tokenization, de-tokenization, visual transformer, multimodal foundation model, code available, huggingface daily},
eprint = {2603.27538},
archiveprefix = {arXiv},
}
{}