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arXiv · 2610.18026

ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression

Yuechuan Chen·Yanda Guo·Xiaoxi Liu·Weilin Ruan·Huanbo Xu·Ruyi Wang·Wei Zhu
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Abstract

Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across heads, overlooking input-dependent differences in budget demand. We propose ChampionKV, an input-adaptive head-wise budget allocator for long-context KV cache compression. It uses the maximum aggregated observation-window attention score as a lightweight empirical proxy for attention concentration, assigning larger budgets to heads with dispersed attention and smaller budgets to concentrated heads. An abnormal-head mask and a floor-budget mechanism improve robustness under weak attention responses. ChampionKV plugs into compressors such as SnapKV and PyramidKV without changing their token-selection procedures. On LongBench and RULER, both Champion variants consistently improve over their SnapKV and PyramidKV base compressors; the improvements are largest and statistically significant on Llama3.1-70B-Instruct, especially under tight budgets, whereas results on the tested 7B/8B models remain competitive but mixed. ChampionKV does not uniformly outperform Ada-KV, whose global cross-head Top-K selection retains an advantage in several settings. Controlled analysis further shows improved retained attention mass over the corresponding base compressors at negligible overhead.

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Yuechuan Chen, Yanda Guo, Xiaoxi Liu, Weilin Ruan, Huanbo Xu, Ruyi Wang, Wei Zhu. 2026-10-13. ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression. https://arxiv.org/abs/2610.18026

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