LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Authors: name: Meituan LongCat Team, name: Bin Xiao, name: Chao Wang, name: Chengjiang Li, name: Chi Zhang, name: Chong Peng, name: Hang Yu, name: Hao Yang, name: Haonan Yan, name: Haoze Sun, name: Haozhe Zhao, name: Hong Liu, name: Hui Su, name: Jiaqi Zhang, name: Jiawei Wang, name: Jing Li, name: Kefeng Zhang, name: Manyuan Zhang, name: Minhao Jing, name: Peng Pei, name: Quan Chen, name: Taofeng Xue, name: Tongxin Pan, name: Xiaotong Li, name: Xiaoyang Li, name: Xiaoyu Zhao, name: Xing Hu, name: Xinyang Lin, name: Xunliang Cai, name: Yan Bai, name: Yan Feng, name: Yanjie Li, name: Yao Qiu, name: Yerui Sun, name: Yifan Lu, name: Ying Luo, name: Yipeng Mei, name: Yitian Chen, name: Yuchen Xie, name: Yufang Liu, name: Yufei Chen, name: Yulei Qian, name: Yuqi Peng, name: Zhihang Yu, name: Zhixiong Han, name: Changran Wang, name: Chen Chen, name: Dian Zheng, name: Fengjiao Chen, name: Ge Yang, name: Haowei Guo, name: Haozhe Wang, name: Hongyu Li, name: Huicheng Jiang, name: Jiale Hong, name: Jialv Zou, name: Jiamu Li, name: Jianping Lin, name: Jiaxing Liu, name: Jie Yang, name: Jing Jin, name: Jun Kuang, name: Juncheng She, name: Kunming Luo, name: Kuofeng Gao, name: Lin Qiu, name: Linsen Guo, name: Mianqiu Huang, name: Qi Li, name: Qian Wang, name: Rumei Li, name: Siyu Ren, name: Wei Wang, name: Wenlong He, name: Xi Chen, name: Xiao Liu, name: Xiaoyu Li, name: Xu Huang, name: Xuanyu Zhu, name: Xuezhi Cao, name: Yaoming Zhu, name: Yifei Cao, name: Yimeng Jia, name: Yizhen Jiang, name: Yufei Gao, name: Zeyang Hu, name: Zhenlong Yuan, name: Zijian Zhang, name: Ziwen Wang
Organization: LongCat

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