Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench

• URL: http://arxiv.org/abs/2604.03455v1
• Date: Fri, 03 Apr 2026 20:58:00 GMT
• Title: Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench
• Abstract summary: We present the first systematic evaluation of lightweight classifier-based routing on the benchmark RAG-Bench citepwang2026ragrouterbench.<n>Our best configuration, TF-IDF with an SVM, achieves a macro-averaged F1 of $mathbf0.928$ and an accuracy of $mathbf93.2%$, while simulating $mathbf28.1%$ token savings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 embeddings \citep{reimers2019sbert}, and hand-crafted structural features, yielding 15 classifier feature combinations. Our best configuration, TF-IDF with an SVM, achieves a macro-averaged F1 of $\mathbf{0.928}$ and an accuracy of $\mathbf{93.2\%}$, while simulating $\mathbf{28.1\%}$ token savings relative to always using the most expensive paradigm. Lexical TF-IDF features outperform semantic sentence embeddings by $3.1$ macro-F1 points, suggesting that surface keyword patterns are strong predictors of query-type complexity. Domain-level analysis reveals that medical queries are hardest to route and legal queries most tractable. These results establish a reproducible query-side baseline and highlight the gap that corpus-aware routing must close.

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