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Life sciences · Preprint
arXiv · August 7, 2026
Early or partial results. Treat as a signal, not a conclusion.
CoinRAG is a proposed method for optimizing Retrieval-Augmented Generation (RAG) systems through fine-grained, reusable KV cache representations of retrieved context. The preprint reports a 5.3% relative improvement in F1 score on LongBench multi-hop tasks under latency constraints, but the work has not been peer-reviewed and is presented as a computational optimization technique without clinical validation or real-world deployment evidence.
Algorithmic optimization with benchmark evaluation; not a randomized or controlled trial.. LongBench multi-hop question answering benchmark tasks; no human subjects or clinical population.. Intervention: CoinRAG: two-stage retrieval to identify query-relevant semantic units within chunks, with compositional reuse of offline-computed fine-grained nugget KV caches.. Compared with: Baseline RAG methods (unspecified); evaluated on LongBench multi-hop question answering tasks..
Average 5.3% relative improvement in answer quality (F1) under standard fast prefill latency budget compared to baselines on LongBench multi-hop question answering tasks CoinRAG achieves new Pareto frontier for latency versus accuracy tradeoff
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An unreviewed computational method paper with algorithmic novelty and benchmark results, but no clinical or patient outcomes, peer review, or comparison to established clinical standards.
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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.
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