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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.12500 |
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| _version_ | 1866916005579587584 |
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| author | Diao, Haiwen Wu, Penghao Deng, Hanming Wang, Jiahao Bai, Shihao Wu, Silei Fan, Weichen Ye, Wenjie Tong, Wenwen Fan, Xiangyu Li, Yan Wang, Yubo Cao, Zhijie Lin, Zhiqian Yang, Zhitao Cai, Zhongang Niu, Yuwei Zhu, Yue Liu, Bo Lv, Chengguang Yu, Haojia Xie, Haozhe Wang, Hongli Fan, Jianan Li, Jiaqi Lu, Jiefan Ni, Jingcheng Xu, Junxiang Liang, Kaihuan Shi, Lianqiang Dai, Linjun Wang, Linyan Qian, Oscar Gao, Peng Liu, Pengfei Sun, Qingping Shen, Rui Wang, Ruisi Ma, Shengnan Yang, Shuang Xie, Siyi Li, Siying Zhong, Tianbo Kong, Xiangli Shi, Xuanke Gao, Yang Yao, Yongqiang Wang, Yves Bai, Zhengqi Lin, Zhengyu Yin, Zixin Sun, Wenxiu Gong, Ruihao Wang, Quan Lu, Lewei Yang, Lei Liu, Ziwei Lin, Dahua |
| author_facet | Diao, Haiwen Wu, Penghao Deng, Hanming Wang, Jiahao Bai, Shihao Wu, Silei Fan, Weichen Ye, Wenjie Tong, Wenwen Fan, Xiangyu Li, Yan Wang, Yubo Cao, Zhijie Lin, Zhiqian Yang, Zhitao Cai, Zhongang Niu, Yuwei Zhu, Yue Liu, Bo Lv, Chengguang Yu, Haojia Xie, Haozhe Wang, Hongli Fan, Jianan Li, Jiaqi Lu, Jiefan Ni, Jingcheng Xu, Junxiang Liang, Kaihuan Shi, Lianqiang Dai, Linjun Wang, Linyan Qian, Oscar Gao, Peng Liu, Pengfei Sun, Qingping Shen, Rui Wang, Ruisi Ma, Shengnan Yang, Shuang Xie, Siyi Li, Siying Zhong, Tianbo Kong, Xiangli Shi, Xuanke Gao, Yang Yao, Yongqiang Wang, Yves Bai, Zhengqi Lin, Zhengyu Yin, Zixin Sun, Wenxiu Gong, Ruihao Wang, Quan Lu, Lewei Yang, Lei Liu, Ziwei Lin, Dahua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12500 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture Diao, Haiwen Wu, Penghao Deng, Hanming Wang, Jiahao Bai, Shihao Wu, Silei Fan, Weichen Ye, Wenjie Tong, Wenwen Fan, Xiangyu Li, Yan Wang, Yubo Cao, Zhijie Lin, Zhiqian Yang, Zhitao Cai, Zhongang Niu, Yuwei Zhu, Yue Liu, Bo Lv, Chengguang Yu, Haojia Xie, Haozhe Wang, Hongli Fan, Jianan Li, Jiaqi Lu, Jiefan Ni, Jingcheng Xu, Junxiang Liang, Kaihuan Shi, Lianqiang Dai, Linjun Wang, Linyan Qian, Oscar Gao, Peng Liu, Pengfei Sun, Qingping Shen, Rui Wang, Ruisi Ma, Shengnan Yang, Shuang Xie, Siyi Li, Siying Zhong, Tianbo Kong, Xiangli Shi, Xuanke Gao, Yang Yao, Yongqiang Wang, Yves Bai, Zhengqi Lin, Zhengyu Yin, Zixin Sun, Wenxiu Gong, Ruihao Wang, Quan Lu, Lewei Yang, Lei Liu, Ziwei Lin, Dahua Computer Vision and Pattern Recognition 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. |
| title | SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.12500 |