arXiv AI By Bojie Li

Models Take Notes at Prefill: KV Cache Can Be Editable and Composable

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arXiv:2606. 17107v1 Announce Type: cross Abstract: Prefix caching reuses prefill only across an exactly shared prefix, so one changed field invalidates the entire downstream cache.

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arXiv AI
Sep 17

Contiguity, Not Importance: Budgeted Repair of Stale KV Caches After Document Edits

The paper investigates how to efficiently repair stale key-value (KV) caches in retrieval‑augmented generation systems after document edits. It proposes a budgeted in‑place recomputation approach and evaluates training‑free position‑selection policies on a factual RAG benchmark. Across three model families, a contiguous edit‑local window consistently recovers most of the post‑edit answer quality while being 13–21 times faster than a full re‑prefill, though its effectiveness diminishes when answer‑bearing text moves downstream.

By Mingyang Mao, Wyatt Mackey, Xiaomin Lin
arXiv Machine Learning
5d ago

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