Abstract
We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, and speech understanding and generation, together with video understanding, within a single architecture. Unlike autoregressive unified models that serialize heterogeneous modalities, or compositional unified models that require orchestration with external modality-specific decoders, Dynin-Omni natively formulates omnimodal modeling as masked diffusion over a shared discrete token space, enabling iterative refinement under bidirectional context. Dynin-Omni adopts a multi-stage training strategy with model-merging-based modality expansion and omnimodal alignment. We evaluate Dynin-Omni across 19 multimodal benchmarks spanning language reasoning, image generation and editing, video understanding, and speech recognition and synthesis. Dynin-Omni achieves 87.6 on GSM8K, 1733.6 on MME-P, 61.4 on VideoMME, 0.87 on GenEval, and 2.1 WER on LibriSpeech test-clean, consistently outperforming existing open-source unified models while remaining competitive with strong modality-specific expert systems. These results demonstrate the potential of masked diffusion as a unified paradigm for any-to-any modeling, providing a flexible foundation for real-time omnimodal systems, unified cross-modal retrieval and generation, and embodied multimodal agents.
Abstract (translated)
我们推出Dynin-Omni，这是首个基于掩码扩散的全模态基础模型，能够在单一架构内统一文本、图像和语音的理解与生成，同时涵盖视频理解。与将异质模态序列化的自回归统一模型，或需要外部模态特定解码器编排的组合式统一模型不同，Dynin-Omni将全模态建模原生地构建为共享离散词元空间上的掩码扩散，支持在双向上下文中的迭代精炼。Dynin-Omni采用基于模型合并的模态扩展与全模态对齐的多阶段训练策略。我们在涵盖语言推理、图像生成与编辑、视频理解、语音识别与合成的19个多模态基准上评估Dynin-Omni。Dynin-Omni在GSM8K上达到87.6，在MME-P上达到1733.6，在VideoMME上达到61.4，在GenEval上达到0.87，在LibriSpeech test-clean上达到2.1 WER，持续优于现有开源统一模型，同时与强大的模态特定专家系统保持竞争力。这些结果证明了掩码扩散作为任意到任意建模统一范式的潜力，为实时全模态系统、统一跨模态检索与生成以及具身多模态智能体提供了灵活的基础。
URL
https://arxiv.org/abs/2604.00007
