Sequential KV Cache Compression Claims 900,000x Beyond TurboQuant — Theoretical Paper Breaks Per-Vector Shannon Limit

arXiv·low signal

ArXiv paper 2604.15356 proposes a two-layer KV cache compression architecture — probabilistic prefix deduplication plus predictive delta coding — that theoretically achieves ~914,000x compression over Google's TurboQuant at the Shannon limit. At 1,000x above the entropy floor (deliberately pessimistic), the ratio remains ~914x over TurboQuant. The 44-point HN story (66 comments) heavily caveats this is purely theoretical with no implementation, but the framework is noteworthy: if even 1% of the theoretical gain materializes in practice, it would dramatically change the economics of long-context inference.