arXiv AI
3d ago

ActKV: Efficient LLM Agents through Action-Guided KV Cache Management

ActKV is a new KV cache compression framework designed for agentic large language model (LLM) inference. It prioritizes cache entries that contribute to action generation, using action-oriented eviction, confidence-driven budget allocation, and page-aware compression to reduce memory usage while preserving accuracy. In long-trace tasks, ActKV retains 98.53% of FullKV’s accuracy using only 25.98% of its peak memory and boosts token and task throughput by 3.97× and 3.58×, respectively.

By Zihan Wang, Cheng Tang, Lei Gong, Chao Wang, Wenqi Lou, Teng Wang, Xuehai Zhou