Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings
arXiv:2605.17524v2 Announce Type: replace Abstract: Binary quantization (BQ) compresses high-dimensional embeddings into one or two bits per coordinate, enabling nearest neighbor search at extreme speed. Yet a striking puzzle persists: BQ achieves competitive recall on contrastive embeddings but fails on others -- and two leading systems adopt diametrically opposite strategies (random rotation vs. preserving coordinate axes) without a common theory explaining when each is appropriate. We address this puzzle by connecting the Gaussian structure recently established for InfoNCE-trained represent
This research addresses a persistent puzzle in AI, as the foundational understanding of binary quantization for contrastive embeddings is still being developed, and existing systems use contradictory approaches.
Improving the efficiency of embedding compression is critical for scaling AI systems, enabling faster nearest neighbor searches, and reducing computational overhead for machine learning tasks across various applications.
A common theoretical framework connecting covariance structure and coordinate heterogeneity in binary quantization can lead to more robust and universally applicable compression techniques for machine learning models.
- · AI compute infrastructure providers
- · Developers of large AI models
- · Search engine companies
- · Database optimization firms
- · Inefficient embedding compression methods
- · Systems relying on high-dimensional raw embeddings
More efficient and scalable AI systems due to optimized embedding storage and retrieval.
Reduced operational costs for AI-powered services and applications, accelerating broader AI adoption.
Potential for new AI applications that were previously computationally prohibitive due to embedding size.
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Read at arXiv cs.LG