From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings

The paper surveys the state of embedding models across different modalities, noting that despite shared mathematical foundations, research remains fragmented. It argues that recent advances—contrastive learning, Transformer architectures, and a shift toward finer granularity—are driving a convergence toward common principles, while modality-specific challenges are still underexplored. The authors propose a framework of three capabilities—coverage, comparability, and compositionality—and outline three unification paths: transformation, alignment, and joint embedding, with the latter advocated as the ultimate goal.

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