FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression

• URL: http://arxiv.org/abs/2507.20030v1
• Date: Sat, 26 Jul 2025 18:20:25 GMT
• Title: FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression
• Authors: Runchao Li, Yao Fu, Mu Sheng, Xianxuan Long, Haotian Yu, Pan Li,
• Abstract summary: FAEDKV is a novel, training-free KV cache compression framework.<n>It preserves both early and recent contextual information.<n>Experiments on LongBench benchmark demonstrate FAEDKV's superiority over existing methods by up to 22%.
• Score: 18.12657364501536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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