KVBoost presents chunk-level key-value cache reuse for large language model inference

The KVBoost paper presents chunk-level key-value cache reuse for HuggingFace-compatible decoder models, allowing cached content to be reused when shared material appears at arbitrary prompt positions rather than only in a leading contiguous prefix.

KVBoost separates positional identity from content identity with dual-hash keys and supports exact and approximate cache matches. It also describes SelectiveRecompute and CacheBlendRecompute to address attention-boundary errors.

The paper reports evaluation on Qwen/Qwen2.5-3B using 1,000 bug-localization samples, including a reported 4.49x reduction in time-to-first-token compared with the stated baseline and a 16% advantage over prefix caching, with the reported accuracy figures showing no loss.

The available evidence is a single arXiv abstract; no source body, quotation, deployment documentation, or implementation availability is supplied, so production readiness and broader model performance cannot be confirmed.