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 0.928 and an accuracy of 93.2%, while simulating 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.
@article{arxiv.2604.03455,
title = {Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench},
author = {Prakhar Bansal and Shivangi Agarwal},
journal= {arXiv preprint arXiv:2604.03455},
year = {2026}
}