Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monolingual contexts. To bridge this critical disconnect, we present a holistic study dedicated to code-switching IR. We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), constructing a dataset via human annotation to capture the authentic naturalness of mixed-language queries. Our evaluation across statistical, dense, and late-interaction paradigms reveals that code-switching acts as a fundamental performance bottleneck, degrading the effectiveness of even robust multilingual models. We demonstrate that this failure stems from substantial divergence in the embedding space between pure and code-switched text. Scaling this investigation, we propose CS-MTEB, a comprehensive benchmark covering 11 diverse tasks, where we observe performance declines of up to 27%. Finally, we show that standard multilingual techniques like vocabulary expansion are insufficient to resolve these deficits completely. These findings underscore the fragility of current systems and establish code-switching as a crucial frontier for future IR optimization.
@article{arxiv.2604.17632,
title = {Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers},
author = {Qingcheng Zeng and Yuheng Lu and Zeqi Zhou and Heli Qi and Puxuan Yu and Fuheng Zhao and Hitomi Yanaka and Weihao Xuan and Naoto Yokoya},
journal= {arXiv preprint arXiv:2604.17632},
year = {2026}
}