By breaking retrieved documents into semantic nuggets and reusing their pre-computed KV caches, you can cut inference costs while improving RAG accuracy—achieving 5.3% better answers under strict latency constraints.
CoinRAG optimizes retrieval-augmented generation by reusing fine-grained KV cache "nuggets" instead of processing entire chunks, reducing computational cost while improving answer quality. It uses two-stage retrieval to identify semantically relevant information units and assembles their cached representations efficiently.