arXiv:2605.02950v2 Announce Type: replace Abstract: Transformer-based semantic encoders are effective for retrieval, but in many deployments the recurring bottleneck is online query encoding rather than offline corpus indexing. This paper studies whether, once a strong teacher representation space and corpus index are fixed, repeated neural query encoding can be replaced by a substantially lighter and analytically explicit estimator. We formulate fixed-teacher lexical-to-semantic encoding as a conditional-mean estimation problem in which the target semantic vector is represented as a noisy mix
This research addresses the growing computational burden of Transformer-based models, a critical issue as AI adoption scale grows. It's published at a time when efficiency and scalability are paramount in AI deployment, especially for retrieval systems.
This paper offers a path to significantly reduce the computational cost of online query encoding for AI systems, making advanced semantic search and retrieval more practical and economical at scale. It tackles a key bottleneck in deploying large language models efficiently.
This approach could replace computationally intensive neural query encoding with a more efficient, explicit estimator once a semantic space is established, shifting the computational burden from online inference to offline setup. It introduces Kernel Affine Hull Machines as a lighter alternative.
- · AI deployment platforms
- · Companies with large semantic search needs
- · Cloud infrastructure providers (due to better resource utilization)
- · Small to medium AI developers
- · Inefficient AI inference chip providers
- · Companies relying solely on dense, high-compute neural inference for all steps
- · Energy producers (in the very long run, due to reduced compute demand)
Reduced operational costs for AI-powered retrieval systems.
Accelerated deployment and broader accessibility of advanced semantic search functionalities across various industries.
Increased competition among AI service providers as computational barriers to entry are lowered, leading to more specialized and cost-effective AI applications.
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Read at arXiv cs.LG