Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench
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. We present the first systematic evaluation of lightweight classifier-based routing on this benchmark. Five classical classifiers are evaluated under three feature regimes, namely, TF-IDF, MiniLM sentence embeddi...
| Authors | Prakhar Bansal, Shivangi Agarwal |
|---|---|
| Primary category | cs.IR |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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