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.

Text embedding models are central to information retrieval and deep research tasks, and their behaviors are strongly shaped by the training objective. While recent systems have largely converged on InfoNCE-style loss functions, there have been continued attempts to design additional loss functions beyond this core objective. One such component is spread-out loss, a batch-level penalty on high pairwise similarity that encourages embeddings to occupy the representation space more evenly. However, in existing systems, this design is usually combined with many other design choices and has not been sufficiently ablated. To isolate its effect, we use spread-out loss as a controlled lens for studying geometric regularization in text embedding training. Across 13 retrieval benchmarks, spread-out improves fine-tuned retrievers over contrastive baselines across architectures and model sizes. We then diagnose where these gains arise and find that they concentrate on short queries, where spread-out reduces query-side directional crowding. Based on this diagnosis, we propose QUARTO, a Query-length Adaptive spread-out loss that uses batch composition to scale the full regularizer more strongly for short-query-dominated batches and less strongly for longer-query-dominated batches. QUARTO further improves retrieval performance over fixed-weight spread-out. Overall, our findings point to the potential of embedding-space regularization and suggest that text-embedding training should explore a broader design space of objectives beyond InfoNCE-style contrastive learning. Project page: https://github.com/JoeZhou-encoding/Spreadout_QUARTO