Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer?
arXiv:2605.27494v1 Announce Type: cross Abstract: Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level KV reuse is now standard in serving stacks such as vLLM, and chunk-level and position-independent reuse have been pushed further by recent systems(RAGCache, TurboRAG, CacheBlend, EPIC, ContextPilot, PCR, LMCache). Output-level semantic answer caches, by contrast, remain fragile: similar prompts can map to different correct answers, retrieved evidence drifts as the corpus is updated, and adversarial
The rapid deployment of RAG systems has highlighted the significant operational costs and latency issues associated with large language models, driving urgent innovation in efficiency. This research addresses a critical vulnerability in output-level caching within these systems, which is becoming increasingly relevant as RAG scales.
Improving the reliability and safety of RAG caching mechanisms directly impacts the cost-effectiveness, performance, and trustworthiness of LLM deployments, which is crucial for broad enterprise adoption. Secure and efficient caching allows for more scalable and economically viable AI applications, accelerating the 'go-to-market' phase of many AI implementations.
The focus is shifting from basic prefix reuse to more sophisticated, output-level semantic caching within RAG, requiring robust grounding mechanisms to ensure accuracy and prevent 'drift' as data evolves. This will lead to more intelligent caching strategies that are aware of the underlying data volatility and prompt variations, ensuring the integrity of cached answers.
- · AI developers
- · Cloud infrastructure providers
- · Enterprises deploying RAG
- · N/A
Increased efficiency and reduced operational costs for retrieval-augmented generation systems.
Faster AI application development cycles and broader adoption of LLM-powered services due to improved economics.
Enhanced trust in AI output, potentially accelerating the collapse of white-collar workflows and SaaS layers.
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Read at arXiv cs.LG