Ablation, Statistical Inference, and Validation for KV-Cache Compression
Machine Learning
2026-06-14 v1 Artificial Intelligence
Information Theory
Abstract
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 separates 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 () adapting to calibration budgets rather than true data rank. (this is an abstract of the abstract thank you )
Cite
@article{arxiv.2607.09683,
title = {Ablation, Statistical Inference, and Validation for KV-Cache Compression},
author = {Paolo D'Alberto and Ashish Siarasao and Elliott Delaye and Rajeev Patwari},
journal= {arXiv preprint arXiv:2607.09683},
year = {2026}
}
Comments
15 pages, 8 figures, minimum number of citations