SOURCE-LINKED INTELLIGENCE
Omni-Interactive Universal Embedder
Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruction-following capabilities. Despite this progress, existing approaches primarily focus on language and image modalities, which also remain the dominant modalities for user-conditioned interactions in current embedders. In this paper, we propose the first Omni-Interactive Universal Embedder (OmniUE), which not only learns a unified embedding space across text, video, and audio by leveraging intermediate-layer representations from
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T12:31:55.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.