AI RESEARCH
Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench
arXiv CS.LG
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ArXi:2604.03455v1 Announce Type: cross Retrieval-Augmented Generation pipelines span a wide range of retrieval strategies that differ substantially in token cost and capability. Selecting the right strategy per query is a practical efficiency problem, yet no routing classifiers have been trained on RAGRouter-Bench \citep{wang2026ragrouterbench}, a recently released benchmark of $7,727$ queries spanning four knowledge domains, each annotated with one of three canonical query types: factual, reasoning, and summarization.