Context Unrolling in Omni Models
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arXiv
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| Format: | Preprint |
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2026
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| author | Yang, Ceyuan Lin, Zhijie Zhao, Yang Xiao, Fei He, Hao Zhao, Qi Deng, Chaorui Li, Kunchang Ding, Zihan Guo, Yuwei Wang, Fuyun Zhu, Fangqi Nie, Xiaonan Zhu, Shenhan Lin, Shanchuan Li, Hongsheng Huang, Weilin Shi, Guang Fan, Haoqi |
| author_facet | Yang, Ceyuan Lin, Zhijie Zhao, Yang Xiao, Fei He, Hao Zhao, Qi Deng, Chaorui Li, Kunchang Ding, Zihan Guo, Yuwei Wang, Fuyun Zhu, Fangqi Nie, Xiaonan Zhu, Shenhan Lin, Shanchuan Li, Hongsheng Huang, Weilin Shi, Guang Fan, Haoqi |
| contents | We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This process enables the model to aggregate complementary information across heterogeneous modalities, facilitating a more faithful approximation of the shared multimodal knowledge manifold and improving downstream reasoning fidelity. As a result, Omni achieves strong performance on both multimodal generation and understanding benchmarks, while demonstrating advanced multimodal reasoning capabilities, including in-context generation of text, image, video, and 3D geometry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21921 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context Unrolling in Omni Models Yang, Ceyuan Lin, Zhijie Zhao, Yang Xiao, Fei He, Hao Zhao, Qi Deng, Chaorui Li, Kunchang Ding, Zihan Guo, Yuwei Wang, Fuyun Zhu, Fangqi Nie, Xiaonan Zhu, Shenhan Lin, Shanchuan Li, Hongsheng Huang, Weilin Shi, Guang Fan, Haoqi Computer Vision and Pattern Recognition We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This process enables the model to aggregate complementary information across heterogeneous modalities, facilitating a more faithful approximation of the shared multimodal knowledge manifold and improving downstream reasoning fidelity. As a result, Omni achieves strong performance on both multimodal generation and understanding benchmarks, while demonstrating advanced multimodal reasoning capabilities, including in-context generation of text, image, video, and 3D geometry. |
| title | Context Unrolling in Omni Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.21921 |