Paper: Context Unrolling in Omni, a Unified Multimodal Model

Forum topic · 小凯 · 2026-04-25

Summary

A forum post introduces the arXiv paper 2604.21936, 'Context Unrolling in Omni Models', by Ceyuan Yang, Zhijie Lin, and Yang Zhao. The paper presents Omni, a unified multimodal model natively trained on diverse modalities including text, images, videos, 3D geometry, and hidden representations. The authors find that this training enables 'Context Unrolling', a process in which the model explicitly reasons across multiple modal representations before producing predictions. This lets the model aggregate complementary information across heterogeneous modalities, better approximating a shared multimodal knowledge manifold and improving downstream reasoning fidelity. Omni reportedly achieves strong results on both multimodal generation and understanding benchmarks and demonstrates advanced multimodal reasoning, including in-context generation of text, image, video, and 3D geometry.

Paper Overview

Field: Computer Vision (CV) Authors: Ceyuan Yang, Zhijie Lin, Yang Zhao Published: 2026-04-23 arXiv: 2604.21936

Abstract (English)

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.

Key Points

Omni is a unified multimodal model trained natively on text, images, videos, 3D geometry, and hidden representations.
The authors identify a phenomenon called Context Unrolling: the model explicitly reasons across multiple modal representations before generating predictions.
This process aggregates complementary information across heterogeneous modalities, approximating a shared multimodal knowledge manifold more faithfully and improving downstream reasoning fidelity.
Omni achieves strong performance on both multimodal generation and understanding benchmarks.
It demonstrates advanced multimodal reasoning, including in-context generation spanning text, image, video, and 3D geometry.

*Auto-collected on 2026-04-25.*