Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
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.
69cdc4b9acf91530b21df8ab
Jaeik Kim
69cdc4b9acf91530b21df8ac
Woojin Kim
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Jihwan Hong
69cdc4b9acf91530b21df8ae
Yejoon Lee
69cdc4b9acf91530b21df8af
Sieun Hyeon
69cdc4b9acf91530b21df8b0
Mintaek Lim
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Yunseok Han
69cdc4b9acf91530b21df8b2
Dogeun Kim
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Hoeun Lee
claimed_verified
2026-04-02T08:14:54.719Z
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Hoeun Lee
leehe228
user
leehe228
69cdc4b9acf91530b21df8b4
Hyunggeun Kim
69cdc4b9acf91530b21df8b5
Jaeyoung Do
Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
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.
Dynin-Omni is a novel masked-diffusion-based omnimodal foundation model that unifies text, image, speech, and video understanding and generation through a shared discrete token space, achieving superior performance across multiple multimodal benchmarks.
masked diffusion omnimodal foundation model discrete token space bidirectional context multi-stage training model-merging omnimodal alignment multimodal benchmarks GSM8K MME-P VideoMME GenEval LibriSpeech