arXiv:2605.23556v1 Announce Type: new Abstract: Why does the low dimensionality of representations, typically $d\approx 1000$, not prevent modern embedding-based retrieval models from scaling to billions, or even trillions, of data points? To answer this question, we study maximal-margin embeddings in the following retrieval model, classically studied in communication complexity [PS86] and more recently in embedding-based retrieval [WBNL26]. Let $A\in \{0,1\}^{N\times n}$ be a matrix indicating whether each of $N$ queries is relevant to each of $n$ documents. We are interested in the largest m
The paper investigates a core technical challenge in AI at a time when large-scale retrieval models are becoming foundational for many AI applications.
Understanding the limits and capabilities of high-dimensional embeddings is crucial for the efficient and scalable development of future AI systems, impacting performance and resource allocation.
This research provides insights into how current retrieval models can maintain efficiency despite representation dimensionality, potentially guiding future architectural choices and optimization strategies.
- · AI developers
- · Cloud providers
- · Information retrieval systems
- · Large language model developers
- · Inefficient AI architectures
- · Data storage costs for redundant representations
Improved understanding of embedding-based retrieval model scalability.
More robust and efficient AI models capable of handling massive datasets.
Potential for new AI applications that were previously bottlenecked by retrieval efficiency.
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Read at arXiv cs.LG