Projection and Quantisation: A Unifying View of Learning to Hash, from Random Projections to the RAG Era
arXiv:2510.04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines that ground large language models, yet the methods that address it have multiplied across communities until they are seldom read as a single field. We argue they form one field with three design choices, and develop the projection-quantisation-organisation (PQO) lens, under which locality-sensitive hashing, learned binary hashing, deep end-to-end hashing, product quantisation, graph-based indexes,
The proliferation of methods for approximate nearest neighbour (ANN) search, particularly with the rise of retrieval-augmented generation (RAG) in large language models, necessitates a unifying framework to advance the field more coherently.
This research provides a foundational analytical framework that could significantly accelerate innovation and efficiency in large-scale retrieval systems, directly impacting the capabilities and costs of advanced AI applications.
The proposed projection-quantisation-organisation (PQO) lens offers a structured way to understand and develop ANN search algorithms, potentially leading to more scalable, accurate, and energy-efficient AI systems.
- · AI developers
- · Cloud providers
- · Data search companies
- · Enterprises adopting RAG
- · Companies relying on inefficient legacy search
- · AI models with poor retrieval capabilities
Improved efficiency and scalability of retrieval-augmented generation (RAG) pipelines for large language models.
Accelerated development of more powerful and reliable AI agents and applications that depend on vast knowledge retrieval.
Reduced compute costs for large-scale AI deployments, potentially expanding AI accessibility and further driving innovation across sectors.
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Read at arXiv cs.LG