Ovis-Embedding: Pushing the Frontiers of Universal Omni-Modal Embeddings

arXiv cs.AISeptember 23, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.25165v1 Announce Type: new Abstract: In this report, we introduce \textbf{Ovis-Embedding}, a state-of-the-art omni-modal embedding family built on native integration of text, image, video, and audio. Instead of assembling separate modality towers, Ovis-Embedding uses a shared multimodal backbone to encode different modalities in a common representation space. Specifically, we make \textbf{three key advances}: (1) \textbf{native omni-modal initialization}: we adopt a pretrained Qwen-omni model as the embedding backbone and adapt it through contrastive training with low-rank initialization; (2) \textbf{data-centric omni-modal training}: we construct a broad, high-quality corpus span

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