This study systematically compares Turbo-Quant and SpectralQuant KV-cache compression, evaluating non-dominated schemes including WHT rotation with Beta Lloyd-Max and QJL through a statistical validation methodology that distinguishes systematic codec differences from implementation variance.
Key findings reveal that while eigenbasis-based methods fail on heavy-tailed data due to covariance instability, they excel in structured regimes, with the effective semantic dimension ($d_{\text{eff}}$) adapting to calibration budgets rather than true data rank.
Blogger's Review: This article employs rigorous statistical validation methods to explore various techniques for KV-cache compression, revealing strengths and weaknesses across different data distributions. The analysis of effective semantic dimensions offers valuable insights for future optimizations.