CoinRAG: 面向长上下文RAG的上下文信息片段KV缓存复用

摘要

最近关于检索增强生成（RAG）的优化研究利用块级KV缓存复用来避免处理冗长的检索上下文以提高效率，但粗粒度块中仍存在显著的信息冗余和噪声。本文提出CoinRAG（面向长上下文RAG的上下文化信息片段KV缓存复用），在低预填充延迟约束下优化帕累托前沿并最大化准确性。该名称隐喻性地反映了我们的核心机制：正如将小面额硬币组合以累积更大价值，CoinRAG以组合方式复用离线计算的细粒度片段缓存，以更具语义相关性且更紧凑的形式高效构建学习到的上下文表示。具体而言，CoinRAG不进行整块编码，而是通过两阶段检索识别检索块中与查询相关的语义单元，并将其切片KV表示与块级上下文无缝组装在一起。在LongBench多跳问答任务上的广泛评估表明，CoinRAG显著降低了运行成本，在标准的快速预填充延迟预算下实现了新的帕累托前沿，答案质量（F1）平均相对提升5.3%，优于其他基线方法。

English

Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise still remain in the coarse-grained chunks. This paper optimizes the Pareto frontier under low prefill latency constraints while maximizing accuracy by proposing CoinRAG (Contextualized Information Nugget KV Cache Reuse for Long-Context RAG). The name metaphorically reflects our core mechanism: much like assembling small tokens (or "coins") to accumulate a larger value, CoinRAG compositionally reuses offline-computed, fine-grained nugget caches to form a learned contextual representation efficiently in a more semantically relevant but compact manner. Specifically, instead of full-chunk encoding, CoinRAG identifies query-relevant semantic units within retrieved chunks through two-stage retrieval and seamlessly assembles their sliced KV representations with a chunk-level context. Extensive evaluations on LongBench multi-hop question answering tasks demonstrate that CoinRAG significantly reduces operational costs and outperforms the other baselines with a new Pareto frontier and an average 5.3% relative improvement in answer quality (F1) under a standard fast prefill latency budget.