ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs

arXiv:2602.07721v3 Announce Type: replace Abstract: KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scale. We introduce ParisKV, a drift-robust, GPU-native KV-cache retrieval framework based on collision-based candidate selection, followed by a quantized inner-product reranking estimator. For million-token contexts, ParisKV supports CPU-offloaded KV caches via Unified Virtual Addressing (UVA), enabling on-demand top-$k$ fetching with minimal overhead. ParisKV matches or outperforms full attention quality

Source: arXiv cs.LG — read the full report at the original publisher.

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