| title | CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG |
|---|---|
| authors | Gyuwan Kim, Cheoneum Park, Tao Yang |
| date | 2026-08-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.07458 |
| affiliation | University of California, Santa Barbara |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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. |
cite
@misc{kim2026coinrag,
title={CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG},
author={Gyuwan Kim and Cheoneum Park and Tao Yang},
year={2026},
eprint={2608.07458},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.07458}
}