arXiv Machine Learning September 29, 2026 By Yuhang Cao, Yanzhou Mu, Chunrong Fang, Zhenyu Chen

CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters

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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.

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