← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1683 · 记录 #1
LazyEviction: Lagged KV Eviction with Attention Pattern Observation for Efficient Long Reasoning
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:50
摘要
Large Language Models (LLMs) exhibit enhanced capabilities by Chain-of-Thought reasoning. However, the extended reasoning sequences introduce significant GPU memory overhead due to increased key-value (KV) cache. Existing KV cache compression methods mitigate memory bottlenecks but struggle in long reasoning tasks. In this paper, we analyze attention patterns in reasoning tasks and reveal a Token Importance Recurrence phenomenon: a large proportion of tokens regain high attention after multiple decoding steps, which is failed to capture by existing works and may lead to unpredictable eviction on such periodically critical tokens. To address this, we propose LazyEviction, an observation window-based lagged eviction framework retaining latent recurring tokens by prioritized eviction based on tokens’ recurrence patterns. Extensive experiments demonstrate that LazyEviction reduces KV cache by 50% 70% while maintaining comparable accuracy, outperforming existing KV cache baselines. Our implementation code can be found at https://github.com/Halo-949/LazyEviction.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
LazyEviction: Lagged KV Eviction with Attention Pattern Observation for Efficient Long Reasoning
Large Language Models (LLMs) exhibit enhanced capabilities by Chain-of-Thought reasoning. However, the extended reasoning sequences introduce significant GPU memory overhead due to increased key-value (KV) cache. Existing KV cache compression methods mitigate memory bottlenecks but struggle in long reasoning tasks. In this paper, we analyze attention patterns in reasoning tasks and reveal a Token Importance Recurrence phenomenon: a large proportion of tokens regain high attention after multiple decoding steps, which is failed to capture by existing works and may lead to unpredictable eviction on such periodically critical tokens. To address this, we propose LazyEviction, an observation window-based lagged eviction framework retaining latent recurring tokens by prioritized eviction based on tokens’ recurrence patterns. Extensive experiments demonstrate that LazyEviction reduces KV cache by 50% 70% while maintaining comparable accuracy, outperforming existing KV cache baselines. Our implementation code can be found at https://github.com/Halo-949/LazyEviction.
查看此版本解读 →查看此版本原文 ↗