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.

翻译：代码切换是全球交流中普遍存在的语言现象，然而现代信息检索系统仍主要针对单语环境设计和评估。为弥合这一关键脱节，我们开展了一项专门针对代码切换信息检索的整体研究。我们引入了CSR-L（代码切换检索基准-精简版），通过人工标注构建数据集以捕捉混合语言查询的真实自然性。在统计、稠密和延迟交互范式上的评估显示，代码切换是一个根本性的性能瓶颈，即使鲁棒的多语言模型也会因此降低效能。我们证明，这种失败源于纯文本与代码切换文本在嵌入空间上的显著差异。为拓展这一研究，我们提出了CS-MTEB，一个涵盖11项多样化任务的综合基准，观察到性能下降高达27%。最后，我们表明词汇扩展等标准多语言技术不足以完全解决这些缺陷。这些发现凸显了当前系统的脆弱性，并将代码切换确立为未来信息检索优化的关键前沿。