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ORDER introduces a framework that dynamically adapts indexing and retrieval strategies in RAG systems based on the query, improving performance in expert domains.
This paper proposes training small language models as multi-agent routers via progressive supervised fine-tuning and reinforcement learning, achieving better retrieval relevance and lower latency compared to LLM baselines that route on intent alone.
A paper proposing a latency-aware LLM query router that jointly optimizes latency, accuracy, and cost using a lightweight latency estimator, achieving up to 40% improvement in accuracy–cost utility while maintaining comparable latencies.
This article explains how Gnutella scaled its query traffic by evolving from flood routing to the Query Routing Protocol (QRP), which uses compact summaries to avoid flooding all peers.
A thread explaining the four essential layers for building production-grade RAG systems beyond simple chunk-embed-retrieve-generate: intelligent query routing, advanced indexing, multi-type retrieval, and continuous evaluation.