English

Minimal-Intervention KV Retention via Set-Conditioned Diversity

Machine Learning 2026-05-19 v2 Computation and Language

Abstract

KV-cache compression at small budgets is a crowded design space spanning cache representation, head-wise routing, compression cadence, decoding behavior, and within-budget scoring. We study seven mechanisms across these five families under matched mean cache on long-form mathematical reasoning (MATH-500~\cite{hendrycks2021math}) with two distilled-reasoning models (Qwen-7B and Llama-8B variants of DeepSeek-R1-Distill~\cite{deepseek2025r1}) at budgets b{64,128}b \in \{64, 128\}. All seven were rejected. We then propose α\alpha, a one-function modification to the TriAttention~\cite{mao2026triattention} retention scorer that replaces argmax-top-kk with greedy facility-location-inspired selection under a V-space redundancy penalty controlled by a single weight λ\lambda. A pre-registered protocol tunes λ\lambda on a frozen development split and confirms on a disjoint held-out split; with λ=0.5\lambda = 0.5, α\alpha clears Bonferroni on two of the four (model, budget) cells (Qwen b=128b{=}128 and Llama b=64b{=}64), no cell is significantly negative, and the pre-registered Branch~A triggers. The finding is asymmetric: a minimal scoring modification beat heavier structural redesigns in this regime, and the combined matched-memory, sympy-graded, held-out confirmation protocol is the evidence standard that made the asymmetry visible.

Keywords

Cite

@article{arxiv.2605.14292,
  title  = {Minimal-Intervention KV Retention via Set-Conditioned Diversity},
  author = {Libo Sun and Po-wei Harn and Peixiong He and Xiao Qin},
  journal= {arXiv preprint arXiv:2605.14292},
  year   = {2026}
}

Comments

15 pages, 3 figures, 3 tables. Code and data: https://github.com/libophd/minimal-kv-retention

R2 v1 2026-07-22T07:11:28.244Z