arxiv
PublishedMay 28, 2026 at 4:00 AM
Clark Hash: Stateless Sparse Johnson-Lindenstrauss Quantization for Neural Embeddings
Publisher summary· verbatim
arXiv:2605.28034v1 Announce Type: new Abstract: Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed Johnson-Lindenstrauss projection, clips the result, and stores a fixed-width scalar-quantized code. Quer
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBridging the Gap: Converting Read Text to Conversational Dialogue57marxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance57marxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting57marxivThe World Won't Wait: Evaluating and Teaching Agents Under a Live Clock57mThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗