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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.18678 |
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| _version_ | 1866910238629691392 |
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| author | Fu, Fengyi Huang, Mengqi Wu, Shaojin Jiang, Yunsheng Huo, Yufei Li, Hao Song, Yinghang Ding, Fei Guo, Jianzhu He, Qian Fu, Zheren Mao, Zhendong Zhang, Yongdong |
| author_facet | Fu, Fengyi Huang, Mengqi Wu, Shaojin Jiang, Yunsheng Huo, Yufei Li, Hao Song, Yinghang Ding, Fei Guo, Jianzhu He, Qian Fu, Zheren Mao, Zhendong Zhang, Yongdong |
| contents | We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text-image-dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collaborative multi-task training. It is grounded in two core principles: unified context modeling and decoupled capability pathways. Specifically, Lance is trained from scratch and employs a dual-stream mixture-of-experts architecture on shared interleaved multimodal sequences, enabling joint context learning while decoupling the pathways for understanding and generation. We further introduce modality-aware rotary positional encoding to mitigate interference among heterogeneous visual tokens and boost cross-task alignment. During training, Lance adopts a staged multi-task training paradigm with capability-oriented objectives and adaptive data scheduling to strengthen both semantic comprehension and visual generation performance. Experimental results demonstrate that Lance substantially outperforms existing open-source unified models in image and video generation, while retaining strong multimodal understanding capabilities. The homepage is available at https://lance-project.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lance: Unified Multimodal Modeling by Multi-Task Synergy Fu, Fengyi Huang, Mengqi Wu, Shaojin Jiang, Yunsheng Huo, Yufei Li, Hao Song, Yinghang Ding, Fei Guo, Jianzhu He, Qian Fu, Zheren Mao, Zhendong Zhang, Yongdong Computer Vision and Pattern Recognition Artificial Intelligence We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text-image-dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collaborative multi-task training. It is grounded in two core principles: unified context modeling and decoupled capability pathways. Specifically, Lance is trained from scratch and employs a dual-stream mixture-of-experts architecture on shared interleaved multimodal sequences, enabling joint context learning while decoupling the pathways for understanding and generation. We further introduce modality-aware rotary positional encoding to mitigate interference among heterogeneous visual tokens and boost cross-task alignment. During training, Lance adopts a staged multi-task training paradigm with capability-oriented objectives and adaptive data scheduling to strengthen both semantic comprehension and visual generation performance. Experimental results demonstrate that Lance substantially outperforms existing open-source unified models in image and video generation, while retaining strong multimodal understanding capabilities. The homepage is available at https://lance-project.github.io. |
| title | Lance: Unified Multimodal Modeling by Multi-Task Synergy |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18678 |