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SenseNova-U1.5: Towards Native Unified Visual Intelligence

arXiv cs.CV Multimodal & Generative Haiwen Diao, Jiahao Wang, Chenjing Ding, Hanming Deng, Jiangnan Chen, Ruixi Zhang, Ruohui Wang, Wenwen Tong, Xiangyu Fan, Yubo Wang, Yue Zhu, Yuwei Niu, Zhengqi Bai, Zhiqian Lin, Zhitao Yang, Zhongang Cai, Bo Yang, Chen Feng, Chengguang Lv, Guangjia Liu, Guanlin Wang, Hanyu Zhang, Haojia Yu, Hongcan Xiao, Hongli Wang, Huan Wu, Huaping Zhong, Jian Fang, Jianan Fan, Jiaqi Li, Jiefan Lu, Jing Zuo, Jingcheng Ni, Junxiang Xu, Linjun Dai, Mutian Xu, Peishen Yan, Penghao Wu, Ruijie Mao, Ruisi Wang, Shihao Bai, Shuang Yang, Shuya Yang, Shuyan Zheng, Silei Wu, Siying Li, Tao Chu, Tianbo Zhong, Tongxi Zhou, Weichao Luo, Weichen Fan, Wenhao Jia, Wenjie Gao, Xiangli Kong, Yan Li, Yang Yong, Zimo Wen, Zixuan Qian, Wenxiu Sun, Ruihao Gong, Quan Wang, Lewei Lu, Lei Yang, Ziwei Liu, Dahua Lin 2026-09-10

TL;DR - SenseNova-U1.5 is an 8B mixture-of-transformers model that unifies visual understanding, reasoning, generation, and editing without separate encoders or VAEs. It suggests that multimodal understanding can transfer directly to complex visual planning and creation in an end-to-end architecture.

Supports native resolutions up to 4K using spatially coherent patch reconstruction and curated generation and editing data.
Uses specialized post-training experts for aesthetics, bilingual text rendering, infographics, and editing, consolidated through multi-expert on-policy distillation.
Reported gains span image fidelity, text rendering, complex composition, multi-reference editing, instruction following, and subject and geometry preservation.
The authors plan to open-source code for supervised fine-tuning, reinforcement learning, and on-policy distillation.

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