@inproceedings{wu-etal-2025-tokenselect,
title = "{T}oken{S}elect: Efficient Long-Context Inference and Length Extrapolation for {LLM}s via Dynamic Token-Level {KV} Cache Selection",
author = "Wu, Wei and
Pan, Zhuoshi and
Fu, Kun and
Wang, Chao and
Chen, Liyi and
Bai, Yunchu and
Wang, Tianfu and
Wang, Zheng and
Xiong, Hui",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1079/",
pages = "21275--21292",
ISBN = "979-8-89176-332-6",
abstract = "Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These issues limit LLMs in long-context scenarios. In this paper, we propose Dynamic Token-Level KV Cache Selection (*TokenSelect*), a training-free method for efficient and accurate long-context inference. *TokenSelect* builds upon the observation of non-contiguous attention sparsity, using QK dot products to measure per-head KV Cache criticality at token-level. By per-head soft voting mechanism, *TokenSelect* selectively involves a few critical KV cache tokens in attention calculation without sacrificing accuracy. To further accelerate *TokenSelect*, we design the Selection Cache based on observations of consecutive Query similarity and implemented the efficient Paged Dot Product Kernel, significantly reducing the selection overhead. A comprehensive evaluation of *TokenSelect* demonstrates up to $23.84\times$ speedup in attention computation and up to $2.28\times$ acceleration in end-to-end latency, while providing superior performance compared to state-of-the-art long-context inference methods."
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<abstract>Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These issues limit LLMs in long-context scenarios. In this paper, we propose Dynamic Token-Level KV Cache Selection (*TokenSelect*), a training-free method for efficient and accurate long-context inference. *TokenSelect* builds upon the observation of non-contiguous attention sparsity, using QK dot products to measure per-head KV Cache criticality at token-level. By per-head soft voting mechanism, *TokenSelect* selectively involves a few critical KV cache tokens in attention calculation without sacrificing accuracy. To further accelerate *TokenSelect*, we design the Selection Cache based on observations of consecutive Query similarity and implemented the efficient Paged Dot Product Kernel, significantly reducing the selection overhead. A comprehensive evaluation of *TokenSelect* demonstrates up to 23.84\times speedup in attention computation and up to 2.28\times acceleration in end-to-end latency, while providing superior performance compared to state-of-the-art long-context inference methods.</abstract>
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%0 Conference Proceedings
%T TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
%A Wu, Wei
%A Pan, Zhuoshi
%A Fu, Kun
%A Wang, Chao
%A Chen, Liyi
%A Bai, Yunchu
%A Wang, Tianfu
%A Wang, Zheng
%A Xiong, Hui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wu-etal-2025-tokenselect
%X Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These issues limit LLMs in long-context scenarios. In this paper, we propose Dynamic Token-Level KV Cache Selection (*TokenSelect*), a training-free method for efficient and accurate long-context inference. *TokenSelect* builds upon the observation of non-contiguous attention sparsity, using QK dot products to measure per-head KV Cache criticality at token-level. By per-head soft voting mechanism, *TokenSelect* selectively involves a few critical KV cache tokens in attention calculation without sacrificing accuracy. To further accelerate *TokenSelect*, we design the Selection Cache based on observations of consecutive Query similarity and implemented the efficient Paged Dot Product Kernel, significantly reducing the selection overhead. A comprehensive evaluation of *TokenSelect* demonstrates up to 23.84\times speedup in attention computation and up to 2.28\times acceleration in end-to-end latency, while providing superior performance compared to state-of-the-art long-context inference methods.
%U https://aclanthology.org/2025.emnlp-main.1079/
%P 21275-21292
Markdown (Informal)
[TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection](https://aclanthology.org/2025.emnlp-main.1079/) (Wu et al., EMNLP 2025)
ACL
- Wei Wu, Zhuoshi Pan, Kun Fu, Chao Wang, Liyi Chen, Yunchu Bai, Tianfu Wang, Zheng Wang, and Hui Xiong. 2025. TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21275–21292, Suzhou, China. Association for Computational Linguistics.