CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation

• URL: http://arxiv.org/abs/2508.02401v1
• Date: Mon, 04 Aug 2025 13:26:16 GMT
• Title: CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation
• Authors: Xiaolin Lin, Jingcun Wang, Olga Kondrateva, Yiyu Shi, Bing Li, Grace Li Zhang,
• Abstract summary: Increasing key-value (KV) cache size poses critical challenges to memory and execution efficiency.<n>Most KV cache compression methods rely on token eviction using all attention heads in Grouped Query Attention (GQA)-based LLMs.<n>We introduce a layer-adaptive KV cache allocation strategy, which consistently outperforms state-of-the-art approaches under various memory budgets.
• Score: 7.119276797399788
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in large language models (LLMs) have significantly boosted long-context processing. However, the increasing key-value (KV) cache size poses critical challenges to memory and execution efficiency. Most KV cache compression methods rely on heuristic token eviction using all attention heads in Grouped Query Attention (GQA)-based LLMs. This method ignores the different functionalities of attention heads, leading to the eviction of critical tokens and thus degrades the performance of LLMs. To address the issue above, instead of using all the attention heads in GQA-based LLMs to determine important tokens as in the previous work, we first identify the attention heads in each layer that are not only capable of retrieving the initial and final tokens of a prompt, but also capable of retrieving important tokens within the text and attending to their surrounding semantic context. Afterwards, we exploit such heads to determine the important tokens and retain their corresponding KV cache pairs. Furthermore, we analyze the cache eviction error of each layer individually and introduce a layer-adaptive KV cache allocation strategy. Experimental results demonstrate the proposed CompressKV consistently outperforms state-of-the-art approaches under various memory budgets on LongBench and Needle-in-a-Haystack benchmarks. Our code is publicly available at: https://github.com/TUDa-HWAI/CompressKV.git.

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