aiqbench analysis
This position paper is squarely about AI: it surveys omnimodal embedding models, the representation layer behind search and recommendation. Its main contribution is a conceptual framework (coverage, comparability, compositionality) for organizing a fragmented literature across text, image, audio, and video, rather than new benchmark results. Readers should treat the roadmap as a useful taxonomy and a prediction about where multimodal representation learning is heading, while noting that joint embedding for true compositionality remains an aspiration that the paper advocates rather than demonstrates.
From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings
arXiv:2611.00172v1 Announce Type: cross 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.
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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2611.00172 [cs.CL]
(or arXiv:2611.00172v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.00172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 232 words
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