arXiv AI By Qiankun Ma, Yanjiang Zhou, Zinan Xiong, Haofei Wang, Zhen Song, Yang Xiang, Ziyao Zhang, Hairong Zheng

GrowPage: On-Demand KV Budgeting for Efficient LLM Reasoning Serving

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GrowPage is an on‑demand key–value (KV) budgeting framework designed to improve large language model (LLM) reasoning serving. It treats KV capacity as a runtime resource, using lightweight dual‑timescale query summaries to track recent and long‑term attention patterns and estimate demand evolution. At each capacity boundary, GrowPage either compresses KV states within the current allocation or acquires an additional physical page, integrating with PagedAttention’s page‑level memory abstraction to maintain continuous batching and prefix caching.

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arXiv Machine Learning
Sep 7

BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference

BeaconKV is a training‑free key‑value cache compression technique for Large Reasoning Models that uses beacon queries—compact representatives of query clusters—to predict which KV pairs will be revisited during long‑horizon reasoning. By focusing on Thought Revisiting Tokens that re‑attend distant context, BeaconKV reduces memory usage up to 5.8× and improves throughput by over 4.3× while largely preserving cache accuracy across multiple open‑source LRMs and reasoning benchmarks.

By Janghyeon Kim, Minsoo Kim, Kyuhong Shim, Jungwook Choi