StepKV introduces a step-aware approach to compressing the key-value cache used during large language model inference, treating reasoning steps as primary units of retention rather than individual tokens. By linking cache entries to the steps that generated them and estimating each step’s utility from trajectory signals, StepKV assigns a combined token‑ and step‑level score to guide pruning. Experiments on multi‑hop question answering and long‑horizon web reasoning show that StepKV maintains accuracy even under tight cache budgets, outperforming token‑level baselines that suffer sharp performance drops.
By Boyu Feng, Jiahong Liu, Yifan Li, Wenhao Yu, Zexuan Qiu, Yuliang Sun, Ming Shen, Xiang Li, Quanyu Dai, Irwin King
arXiv:2607. 10582v1 Announce Type: cross Abstract: Large language model (LLM) agents accumulate heterogeneous context, including system instructions, plans, user turns, retrieved documents, tool outputs, and intermediate reasoning, whose key-value (KV) cache can become a major memory bottleneck.
By Venkatesha Matam, Keon Kim
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
arXiv:2606. 09916v1 Announce Type: cross Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning.
By Junjie Li, Jiong Lou, Jie Li
arXiv:2606. 01065v1 Announce Type: cross Abstract: Modern KV cache management assumes the chatbot workload: prompts arrive once and the cache grows append-only, so prefix caching and forward-only eviction are correct by construction.
By Bole Ma, Jan Eitzinger, Harald Koestler
arXiv:2609.10266v1 Announce Type: new
Abstract: LLM serving systems already reuse KV caches, but only when the reused text sits at the very start of the prompt. Two growing workloads break this condi...
By Xi Shi, Qian Lou