@inproceedings{lu-etal-2026-one-size,
title = "One Size Does Not Fit All: Token-Wise Adaptive Compression for {KV} Cache",
author = "Lu, Liming and
Qiu, Kaixi and
Zhou, Jiayu and
Kai, Jushi and
Zhang, Haoyan and
Wang, Jingzhi and
Wang, Huanyu and
Jiang, Bo and
Leng, Jingwen and
He, Ziwei and
Lin, Zhouhan",
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.524/",
pages = "12100--12114",
ISBN = "979-8-89176-459-0",
abstract = "Despite the remarkable progress of Large Language Models (LLMs), the escalating memory footprint of the Key-Value (KV) cache remains a critical bottleneck for efficient inference. While dimensionality reduction offers a promising compression avenue, existing approaches typically either necessitate prohibitively expensive pre-training from scratch or suffer from severe performance deterioration under high compression regimes. In this work, we propose DynaKV, a novel post-training framework for low-rank KV cache compression. To the best of our knowledge, DynaKV is the first low-rank method to dynamically allocate compression rates to individual tokens according to their semantic meaning, which allows it to achieve better fidelity at aggressive compression ratios. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art compression techniques, achieving significant memory reduction while maintaining competitive generation quality. Furthermore, our approach is orthogonal to sequence-level pruning methods. When integrated with SnapKV, DynaKV retains only 6{\%} of the KV cache while maintaining 94{\%} of the baseline performance on the LongBench benchmark."
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<abstract>Despite the remarkable progress of Large Language Models (LLMs), the escalating memory footprint of the Key-Value (KV) cache remains a critical bottleneck for efficient inference. While dimensionality reduction offers a promising compression avenue, existing approaches typically either necessitate prohibitively expensive pre-training from scratch or suffer from severe performance deterioration under high compression regimes. In this work, we propose DynaKV, a novel post-training framework for low-rank KV cache compression. To the best of our knowledge, DynaKV is the first low-rank method to dynamically allocate compression rates to individual tokens according to their semantic meaning, which allows it to achieve better fidelity at aggressive compression ratios. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art compression techniques, achieving significant memory reduction while maintaining competitive generation quality. Furthermore, our approach is orthogonal to sequence-level pruning methods. When integrated with SnapKV, DynaKV retains only 6% of the KV cache while maintaining 94% of the baseline performance on the LongBench benchmark.</abstract>
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%0 Conference Proceedings
%T One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache
%A Lu, Liming
%A Qiu, Kaixi
%A Zhou, Jiayu
%A Kai, Jushi
%A Zhang, Haoyan
%A Wang, Jingzhi
%A Wang, Huanyu
%A Jiang, Bo
%A Leng, Jingwen
%A He, Ziwei
%A Lin, Zhouhan
%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 lu-etal-2026-one-size
%X Despite the remarkable progress of Large Language Models (LLMs), the escalating memory footprint of the Key-Value (KV) cache remains a critical bottleneck for efficient inference. While dimensionality reduction offers a promising compression avenue, existing approaches typically either necessitate prohibitively expensive pre-training from scratch or suffer from severe performance deterioration under high compression regimes. In this work, we propose DynaKV, a novel post-training framework for low-rank KV cache compression. To the best of our knowledge, DynaKV is the first low-rank method to dynamically allocate compression rates to individual tokens according to their semantic meaning, which allows it to achieve better fidelity at aggressive compression ratios. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art compression techniques, achieving significant memory reduction while maintaining competitive generation quality. Furthermore, our approach is orthogonal to sequence-level pruning methods. When integrated with SnapKV, DynaKV retains only 6% of the KV cache while maintaining 94% of the baseline performance on the LongBench benchmark.
%U https://aclanthology.org/2026.emnlp-main.524/
%P 12100-12114
Markdown (Informal)
[One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache](https://aclanthology.org/2026.emnlp-main.524/) (Lu et al., EMNLP 2026)
ACL
- Liming Lu, Kaixi Qiu, Jiayu Zhou, Jushi Kai, Haoyan Zhang, Jingzhi Wang, Huanyu Wang, Bo Jiang, Jingwen Leng, Ziwei He, and Zhouhan Lin. 2026. One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 12100–12114, Budapest, Hungary. Association for Computational Linguistics.