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    <abstract>Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.</abstract>
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%0 Conference Proceedings
%T SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation
%A Wu, Jialong
%A Wang, Zhenglin
%A Zhang, Linhai
%A Lai, Yilong
%A He, Yulan
%A Zhou, Deyu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wu-etal-2025-scope
%X Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.
%R 10.18653/v1/2025.acl-long.529
%U https://aclanthology.org/2025.acl-long.529/
%U https://doi.org/10.18653/v1/2025.acl-long.529
%P 10775-10790

Markdown (Informal)

[SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation](https://aclanthology.org/2025.acl-long.529/) (Wu et al., ACL 2025)

• SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (Wu et al., ACL 2025)

ACL

• Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai, Yulan He, and Deyu Zhou. 2025. SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10775–10790, Vienna, Austria. Association for Computational Linguistics.