Summary
A forum post on zhichai.net introduces the paper 'Context Unrolling in Omni Models' (arXiv: 2604.21921), a computer vision research work from a large team including Ceyuan Yang, Zhijie Lin, and Haoqi Fan, published April 23, 2026. The paper presents Omni, a unified multimodal model natively trained across multiple modalities: text, images, video, 3D geometry, and hidden representations. The key finding is a phenomenon called Context Unrolling, in which the model explicitly reasons across heterogeneous multimodal representations before producing predictions. This mechanism lets the model aggregate complementary information across modalities, approximate a shared multimodal knowledge manifold more faithfully, and improve downstream reasoning fidelity. As a result, Omni achieves strong performance on multimodal generation and understanding benchmarks and demonstrates advanced multimodal reasoning capabilities, including contextual generation spanning text, images, video, and 3D geometry. The post contains the paper metadata, author list, and a Chinese-language abstract translated from the original paper.
Paper Overview
Research Area: CV
Authors: Ceyuan Yang, Zhijie Lin, Yang Zhao, Fei Xiao, Hao He, Qi Zhao, Chaorui Deng, Kunchang Li, Zihan Ding, Yuwei Guo, Fuyun Wang, Fangqi Zhu, Xiaonan Nie, Shenhan Zhu, Shanchuan Lin, Hongsheng Li, Weilin Huang, Guang Shi, Haoqi Fan
Published: 2026-04-23
arXiv: 2604.21921
Abstract (translated from Chinese)
We present Omni, a unified multimodal model natively trained across multiple modalities, including text, images, video, 3D geometry, and hidden representations. We find that this training enables Context Unrolling, where the model explicitly reasons across multimodal representations before producing predictions. This process allows the model to aggregate complementary information across heterogeneous modalities, facilitates a more faithful approximation of a shared multimodal knowledge manifold, and improves downstream reasoning fidelity. As a result, Omni achieves strong performance on multimodal generation and understanding benchmarks, while demonstrating advanced multimodal reasoning capabilities, including contextual generation across text, images, video, and 3D geometry.
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*Auto-collected on 2026-04-27*
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