@inproceedings{wang-etal-2026-beyond-homogeneous,
title = "Beyond Homogeneous Attention: Memory-Efficient {LLM}s via {F}ourier-Approximated {KV} Cache",
author = "Wang, Qiqi and
He, Siyang and
Liu, Xiaoran and
Li, Ruixiao and
Song, Yuerong and
Liu, Zhigeng and
Huang, Mianqiu and
Huang, Zengfeng and
Guo, Qipeng and
He, Ziwei and
Qiu, Xipeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2162/",
pages = "49187--49205",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rely on attention-guided token pruning, often sacrificing accuracy or introducing computational overhead. We propose FourierAttention, a training-free framework that exploits the heterogeneous roles of transformer head dimensions: lower dimensions prioritize local context, while upper ones capture long-range dependencies. By projecting the long-context-insensitive dimensions onto orthogonal Fourier bases, FourierAttention approximates their temporal evolution with fixed-length spectral coefficients. Evaluations on Llama and Qwen models show FourierAttention achieves the best long-context accuracy on LongBench and Needle-In-A-Haystack (NIAH). Besides, a custom Triton kernel, FlashFourierAttention, is designed to optimize memory via streamlined read-write operations, enabling efficient deployment without performance compromise."
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%0 Conference Proceedings
%T Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache
%A Wang, Qiqi
%A He, Siyang
%A Liu, Xiaoran
%A Li, Ruixiao
%A Song, Yuerong
%A Liu, Zhigeng
%A Huang, Mianqiu
%A Huang, Zengfeng
%A Guo, Qipeng
%A He, Ziwei
%A Qiu, Xipeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-beyond-homogeneous
%X Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rely on attention-guided token pruning, often sacrificing accuracy or introducing computational overhead. We propose FourierAttention, a training-free framework that exploits the heterogeneous roles of transformer head dimensions: lower dimensions prioritize local context, while upper ones capture long-range dependencies. By projecting the long-context-insensitive dimensions onto orthogonal Fourier bases, FourierAttention approximates their temporal evolution with fixed-length spectral coefficients. Evaluations on Llama and Qwen models show FourierAttention achieves the best long-context accuracy on LongBench and Needle-In-A-Haystack (NIAH). Besides, a custom Triton kernel, FlashFourierAttention, is designed to optimize memory via streamlined read-write operations, enabling efficient deployment without performance compromise.
%U https://aclanthology.org/2026.emnlp-main.2162/
%P 49187-49205
Markdown (Informal)
[Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache](https://aclanthology.org/2026.emnlp-main.2162/) (Wang et al., EMNLP 2026)
ACL
- Qiqi Wang, Siyang He, Xiaoran Liu, Ruixiao Li, Yuerong Song, Zhigeng Liu, Mianqiu Huang, Zengfeng Huang, Qipeng Guo, Ziwei He, and Xipeng Qiu. 2026. Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49187–49205, Budapest, Hungary. Association for Computational Linguistics.