First omnimodal embeddings survey maps convergence across text, vision, audio

_reachsumit · x · 2026-11-04

A new arXiv position paper argues that embedding research across text, vision, audio, graphs and more is converging on shared principles—driven by contrastive learning, Transformer architectures, and a granularity shift—despite developing in isolated communities.

Proposes a framework of three progressively harder capabilities: coverage, comparability, and compositionality.
Maps three paths toward unification: transformation (broadens coverage), alignment (enables comparability), and joint embedding—the advocated terminus, aiming at compositionality.
The first unified, cross-cutting treatment of omnimodal embedding research, with an accompanying GitHub repo.

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First omnimodal embeddings survey maps convergence across text, vision, audio

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