想给RAG省算力又不想掉精度？CoinRAG把检索结果拆成小碎块做缓存复用，LongBench上F1平均提了5.3%，值得一看。
CoinRAG是一种针对长上下文检索增强生成（RAG）的KV缓存复用优化方法，通过两阶段检索识别查询相关的语义单元，并组合复用其切片KV表示，避免处理冗长检索上下文。在LongBench多跳问答任务上，CoinRAG相比基线实现了新的帕累托前沿，在标准快速预填充延迟预算下平均F1相对提升5.3%，同时显著降低运营成本。该方法将粗粒度块级缓存细化为语义碎块级，减少了信息冗余与噪声。
CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG
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.