arXiv:2606.09331v1 Announce Type: cross Abstract: Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities differ in data distribution, architecture, and optimization dynamics. In this work, we present Conan-embedding-v3, a decouple--fuse--recover framework for omni-modal retrieval. Conan-embedding-v3 first trains modality specialists independently and fuses their task vectors into a single dense backbone, a strategy we call Decoupled Specialist Fusion. We show that this
The continuous drive for more performant and versatile AI models, particularly in multi-modal understanding, is pushing research towards novel architectural fusion techniques.
Achieving a truly omni-modal embedding space would significantly simplify complex AI applications involving diverse data types, enhancing efficiency and generalization beyond current capabilities.
The proposed 'decouple--fuse--recover' framework via Decoupled Specialist Fusion introduces a new methodology for integrating modality-specific AI models into a single, unified backbone.
- · AI developers
- · Omni-modal retrieval platform providers
- · Companies with diverse data assets
- · Monolithic, single-modality AI models
- · Companies relying on fractured AI data pipelines
Improved performance and reduced complexity for multi-modal AI systems.
Accelerated development of AI agents capable of understanding and integrating information from all sensory inputs.
New classes of AI applications that were previously impossible due to the difficulty of unifying diverse data modalities.
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