CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG

Analysis

Why This Paper Matters

Long-context RAG systems face a critical trade-off between latency and accuracy. Traditional chunk-level KV cache reuse reduces computation but retains noise and redundancy, degrading answer quality. CoinRAG addresses this by moving to a finer granularity—semantic nuggets—allowing more precise reuse of cached representations. This is significant because it directly targets the Pareto frontier, showing that efficiency and accuracy can be improved simultaneously.

The paper's approach is metaphorically named 'coins' to reflect assembling small, valuable units into a larger whole. This concept aligns with the growing trend of modular and compositional inference, where precomputed components are dynamically combined. By demonstrating gains on multi-hop QA, which requires complex reasoning over multiple pieces of evidence, CoinRAG shows practical value for challenging tasks.

Technical Contributions

• Nugget-level KV cache reuse: Instead of caching entire chunks, CoinRAG caches fine-grained semantic units (nuggets) that can be selectively reused.
• Two-stage retrieval: A method to identify query-relevant nuggets within retrieved chunks, ensuring only pertinent information is assembled.
• Compositional assembly: Sliced KV representations of nuggets are combined with chunk-level context to form a compact, contextually rich representation.
• Pareto frontier optimization: The method is designed to achieve better accuracy-latency trade-offs, specifically under low prefill latency constraints.

Results

The paper reports evaluations on LongBench multi-hop question answering tasks. CoinRAG achieves a new Pareto frontier, meaning it provides better accuracy for a given latency budget compared to baselines. Specifically, it shows an average 5.3% relative improvement in F1 score under a standard fast prefill latency budget. Additionally, it significantly reduces operational costs, likely due to reduced computation from cache reuse.

Significance

CoinRAG contributes to the efficiency of LLM inference, particularly for RAG systems that handle long contexts. By enabling fine-grained cache reuse, it reduces the need to process entire retrieved documents, which is a major bottleneck. This could make RAG more viable for real-time applications and reduce energy consumption. The approach also opens avenues for further research into semantic-level caching and compositional inference, potentially influencing future system designs for scalable AI services.