arXiv Machine Learning
Sep 29

CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters

CacheReforge is a method for recovering stale key‑value (KV) caches in large language models when lightweight adapters evolve. It represents stale caches as layer‑wise mixed‑version objects and uses adapter anchors, sensitivity calibration, drift accumulation, and restart boundaries to decide between direct reuse, bounded recomputation, or full suffix recovery. Experiments on Qwen2.5 models with continual LoRA updates show a 92.4% reduction in mean KL divergence while only recomputing 5.44% of layers and cutting cache‑maintenance time by 93.2% compared to full prefill.

By Yuhang Cao, Yanzhou Mu, Chunrong Fang, Zhenyu Chen
arXiv Machine Learning
Sep 29

Evaluating the accuracy of KV cache reuse techniques

The paper discusses position‑independent KV cache reuse, a technique designed to cut latency in retrieval‑augmented generation by reusing chunk‑level KV caches across prompts. It argues that current evaluation methods overstate the accuracy of such reuse because they do not accurately capture the loss of accuracy, and that existing datasets lack the necessary reuse dynamics for thorough testing. To remedy this, the authors propose a new evaluation methodology that unambiguously measures accuracy loss and introduce Boxoffice, a tool that programmatically creates datasets with challenging KV cache reuse patterns.

By Samuel Cestola, Tianxiang Xia, Pengfei Zheng, Weiyan Zheng, Bo Wang, Yi Zhao, Diego Didona
arXiv AI
Oct 15

LatentRemedy: Efficient Remedy For Non-Prefix KV Cache Reuse in Multi-Agent Systems

LatentRemedy is a method designed to improve inference efficiency in multi‑agent systems that share non‑prefix KV caches. By inserting a small number of task‑conditioned latent tokens generated via an MLP, the target agent can better interpret reused context without recomputing the shared block. Experiments demonstrate that LatentRemedy consistently outperforms existing reuse baselines on conflicting tasks while keeping TTFT close to direct reuse as cache size grows.

By Zhiwei Chen, Zhongtao Rao, Waikit Xiu, Yuxiang Zhang, Shuting Cui, Jian Chen, Tianxiang Zhao