MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning

• URL: http://arxiv.org/abs/2512.19206v1
• Date: Mon, 22 Dec 2025 09:44:26 GMT
• Title: MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning
• Authors: Tao Zhang, Ziqian Zeng, Hao Peng, Huiping Zhuang, Cen Chen,
• Abstract summary: Long Chain-of-Thought (CoT) reasoning has significantly advanced the capabilities of Large Language Models (LLMs)<n>Existing low-bit quantization methods often exhibit severe performance degradation on complex reasoning tasks.<n>We propose MixKVQ, a novel plug-and-play method that introduces a lightweight, query-aware algorithm to identify and preserve critical key channels.
• Score: 30.527521568636242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long Chain-of-Thought (CoT) reasoning has significantly advanced the capabilities of Large Language Models (LLMs), but this progress is accompanied by substantial memory and latency overhead from the extensive Key-Value (KV) cache. Although KV cache quantization is a promising compression technique, existing low-bit quantization methods often exhibit severe performance degradation on complex reasoning tasks. Fixed-precision quantization struggles to handle outlier channels in the key cache, while current mixed-precision strategies fail to accurately identify components requiring high-precision representation. We find that an effective low-bit KV cache quantization strategy must consider two factors: a key channel's intrinsic quantization difficulty and its relevance to the query. Based on this insight, we propose MixKVQ, a novel plug-and-play method that introduces a lightweight, query-aware algorithm to identify and preserve critical key channels that need higher precision, while applying per-token quantization for value cache. Experiments on complex reasoning datasets demonstrate that our approach significantly outperforms existing low-bit methods, achieving performance comparable to a full-precision baseline at a substantially reduced memory footprint.

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