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 m
Embedding research across modalities is converging toward shared principles via contrastive learning and Transformers, and a framework of coverage, comparability, and compositionality is proposed to unify it through transformation, alignment, and joint embedding.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #11 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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It was first seen on Nov 3, 2026 and has been on the list on 2 days (13 checks) since.