Mixing Importance with Diversity: Joint Optimization for KV Cache Compression in Large Vision-Language Models

• URL: http://arxiv.org/abs/2510.20707v1
• Date: Thu, 23 Oct 2025 16:17:47 GMT
• Title: Mixing Importance with Diversity: Joint Optimization for KV Cache Compression in Large Vision-Language Models
• Authors: Xuyang Liu, Xiyan Gui, Yuchao Zhang, Linfeng Zhang,
• Abstract summary: textttMixKV is a novel method that mixes importance with diversity for optimized KV cache compression in vision-language models.<n>Under extreme compression, textttMixKV improves baseline methods by an average of textbf5.1% across five multi-modal understanding benchmarks.
• Score: 14.603288559638614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent large vision-language models (LVLMs) demonstrate remarkable capabilities in processing extended multi-modal sequences, yet the resulting key-value (KV) cache expansion creates a critical memory bottleneck that fundamentally limits deployment scalability. While existing KV cache compression methods focus on retaining high-importance KV pairs to minimize storage, they often overlook the modality-specific semantic redundancy patterns that emerge distinctively in multi-modal KV caches. In this work, we first analyze how, beyond simple importance, the KV cache in LVLMs exhibits varying levels of redundancy across attention heads. We show that relying solely on importance can only cover a subset of the full KV cache information distribution, leading to potential loss of semantic coverage. To address this, we propose \texttt{MixKV}, a novel method that mixes importance with diversity for optimized KV cache compression in LVLMs. \texttt{MixKV} adapts to head-wise semantic redundancy, selectively balancing diversity and importance when compressing KV pairs. Extensive experiments demonstrate that \texttt{MixKV} consistently enhances existing methods across multiple LVLMs. Under extreme compression (budget=64), \texttt{MixKV} improves baseline methods by an average of \textbf{5.1\%} across five multi-modal understanding benchmarks and achieves remarkable gains of \textbf{8.0\%} and \textbf{9.0\%} for SnapKV and AdaKV on GUI grounding tasks, all while maintaining comparable inference efficiency. Furthermore, \texttt{MixKV} extends seamlessly to LLMs with comparable performance gains. Our code is available at \href{https://github.com/xuyang-liu16/MixKV}{\textcolor{citeblue}{https://github.com/xuyang-liu16/MixKV}}.

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