CacheClip Framework Accelerates RAG with KV Cache Reuse
A new framework called CacheClip addresses time-to-first-token (TTFT) bottlenecks in Retrieval-Augmented Generation (RAG) systems by reusing KV cache. Existing methods like prefix caching and direct precomputation suffer from trade-offs between speed and quality. CacheClip leverages small auxiliary LLMs that exhibit similar last-layer attention distributions to primary LLMs, enabling efficient identification of tokens critical for restoring inter-chunk attention. This improves response quality on cross-chunk reasoning tasks while achieving fast TTFT. The paper is available on arXiv under identifier 2510.10129.
Key facts
- CacheClip is a novel framework for accelerating RAG systems.
- It addresses TTFT bottlenecks caused by long input sequences.
- Existing KV cache reuse methods face trade-offs between speed and quality.
- Prefix caching requires identical prefixes, rare in RAG scenarios.
- Direct precomputation sacrifices quality due to missing inter-chunk attention.
- CacheClip uses small auxiliary LLMs with similar attention distributions to primary LLMs.
- It improves response quality on cross-chunk reasoning tasks.
- The paper is published on arXiv with identifier 2510.10129.
Entities
Institutions
- arXiv