SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Authors: name: Haiwen Diao, name: Penghao Wu, name: Hanming Deng, name: Jiahao Wang, name: Shihao Bai, name: Silei Wu, name: Weichen Fan, name: Wenjie Ye, name: Wenwen Tong, name: Xiangyu Fan, name: Yan Li, name: Yubo Wang, name: Zhijie Cao, name: Zhiqian Lin, name: Zhitao Yang, name: Zhongang Cai, name: Yuwei Niu, name: Yue Zhu, name: Bo Liu, name: Chengguang Lv, name: Haojia Yu, name: Haozhe Xie, name: Hongli Wang, name: Jianan Fan, name: Jiaqi Li, name: Jiefan Lu, name: Jingcheng Ni, name: Junxiang Xu, name: Kaihuan Liang, name: Lianqiang Shi, name: Linjun Dai, name: Linyan Wang, name: Oscar Qian, name: Peng Gao, name: Pengfei Liu, name: Qingping Sun, name: Rui Shen, name: Ruisi Wang, name: Shengnan Ma, name: Shuang Yang, name: Siyi Xie, name: Siying Li, name: Tianbo Zhong, name: Xiangli Kong, name: Xuanke Shi, name: Yang Gao, name: Yongqiang Yao, name: Yves Wang, name: Zhengqi Bai, name: Zhengyu Lin, name: Zixin Yin, name: Wenxiu Sun, name: Ruihao Gong, name: Quan Wang, name: Lewei Lu, name: Lei Yang, name: Ziwei Liu, name: Dahua Lin
Organization: SenseNova

Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fragmented architectures, cascaded pipelines, and misaligned representation spaces. We argue that this divide is not merely an engineering artifact, but a structural limitation that hinders the emergence of native multimodal intelligence. Hence, we introduce SenseNova-U1, a native unified multimodal paradigm built upon NEO-unify, in which understanding and generation evolve as synergistic views of a single underlying process. We launch two native unified variants, SenseNova-U1-8B-MoT and SenseNova-U1-A3B-MoT, built on dense (8B) and mixture-of-experts (30B-A3B) understanding baselines, respectively. Designed from first principles, they rival top-tier understanding-only VLMs across text understanding, vision-language perception, knowledge reasoning, agentic decision-making, and spatial intelligence. Meanwhile, they deliver strong semantic consistency and visual fidelity, excelling in conventional or knowledge-intensive any-to-image (X2I) synthesis, complex text-rich infographic generation, and interleaved vision-language generation, with or without think patterns. Beyond performance, we show detailed model design, data preprocessing, pre-/post-training, and inference strategies to support community research. Last but not least, preliminary evidence demonstrates that our models extend beyond perception and generation, performing strongly in vision-language-action (VLA) and world model (WM) scenarios. This points toward a broader roadmap where models do not translate between modalities, but think and act across them in a native manner. Multimodal AI is no longer about connecting separate systems, but about building a unified one and trusting the necessary capabilities to emerge from within.