Artificial Intelligence #learning to hash#random projections
New Unifying Lens for Learning to Hash Could Cut Memory Costs in Large-Scale Retrieval
A new arXiv paper from researcher Sean Moran proposes a unifying lens for approximate nearest-neighbour search, framing all methods as variations of projection, quantisation, and organisation. The work introduces the open BitBudget benchmark and finds that quantisation delivers the largest memory savings, with one-bit codes matching uncompressed quality for most embedders at 1/32 the size. The study also shows supervised eight-byte codes can more than double retrieval quality over two-kilobyte floats.
Jun 16, 2026 1 source