Ablation, Statistical Inference, and Validation for KV-Cache Compression

Jul 14, 2026

This study systematically compares Turbo-Quant and SpectralQuant KV-cache compression methods, using a statistical validation methodology to evaluate non-dominated schemes. The research finds that eigenbasis-based methods perform poorly on heavy-tailed data due to covariance instability but excel in structured regimes, with the effective semantic dimension adapting to calibration budgets rather than true data rank.

Why it matters: The work provides a rigorous framework for evaluating KV-cache compression techniques, which is important for optimizing memory and latency in large language model inference.

Full story at: arXiv Machine Learning ↗