FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912504135811072 |
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| author | Li, Runchao Fu, Yao Sheng, Mu Long, Xianxuan Yu, Haotian Li, Pan |
| author_facet | Li, Runchao Fu, Yao Sheng, Mu Long, Xianxuan Yu, Haotian Li, Pan |
| contents | The efficacy of Large Language Models (LLMs) in long-context tasks is often hampered by the substantial memory footprint and computational demands of the Key-Value (KV) cache. Current compression strategies, including token eviction and learned projections, frequently lead to biased representations -- either by overemphasizing recent/high-attention tokens or by repeatedly degrading information from earlier context -- and may require costly model retraining. We present FAEDKV (Frequency-Adaptive Infinite-Window for KV cache), a novel, training-free KV cache compression framework that ensures unbiased information retention. FAEDKV operates by transforming the KV cache into the frequency domain using a proposed Infinite-Window Fourier Transform (IWDFT). This approach allows for the equalized contribution of all tokens to the compressed representation, effectively preserving both early and recent contextual information. A preliminary frequency ablation study identifies critical spectral components for layer-wise, targeted compression. Experiments on LongBench benchmark demonstrate FAEDKV's superiority over existing methods by up to 22\%. In addition, our method shows superior, position-agnostic retrieval accuracy on the Needle-In-A-Haystack task compared to compression based approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_20030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression Li, Runchao Fu, Yao Sheng, Mu Long, Xianxuan Yu, Haotian Li, Pan Computation and Language The efficacy of Large Language Models (LLMs) in long-context tasks is often hampered by the substantial memory footprint and computational demands of the Key-Value (KV) cache. Current compression strategies, including token eviction and learned projections, frequently lead to biased representations -- either by overemphasizing recent/high-attention tokens or by repeatedly degrading information from earlier context -- and may require costly model retraining. We present FAEDKV (Frequency-Adaptive Infinite-Window for KV cache), a novel, training-free KV cache compression framework that ensures unbiased information retention. FAEDKV operates by transforming the KV cache into the frequency domain using a proposed Infinite-Window Fourier Transform (IWDFT). This approach allows for the equalized contribution of all tokens to the compressed representation, effectively preserving both early and recent contextual information. A preliminary frequency ablation study identifies critical spectral components for layer-wise, targeted compression. Experiments on LongBench benchmark demonstrate FAEDKV's superiority over existing methods by up to 22\%. In addition, our method shows superior, position-agnostic retrieval accuracy on the Needle-In-A-Haystack task compared to compression based approaches. |
| title | FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.20030 |