[Submitted on 30 Oct 2026]
Title:From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings
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Abstract:Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Despite shared mathematical foundations, research on embedding models remains fragmented by modality: the communities develop largely in isolation, seldom exchanging insights. We argue that this fragmentation obscures a deeper convergence. Driven by contrastive learning, Transformer architectures, and a granularity shift, embedding research across modalities is converging toward a common set of principles, even as modality-specific challenges remain underexplored. To reason across this convergence without conflating it, we propose a framework of three progressively harder capabilities: coverage, comparability, and compositionality. Three paths toward unification satisfy these in turn: transformation broadens coverage, alignment enables comparability, and joint embedding, the terminus we advocate, aims at compositionality. This position paper traces the resulting evolution, providing the first unified, cross-cutting treatment of omnimodal embedding research.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.00172 [cs.CL]
(or arXiv:2611.00172v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.00172
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziyin Zhang [view email] [v1] Fri, 30 Oct 2026 14:07:34 UTC (3,726 KB)
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