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.

Instruction-following information retrieval (IF-IR) requires retrievers to satisfy both topical intent and instance-specific relevance constraints. We propose dual-view training, a data synthesis strategy based on polarity reversal. Given a query, an instruction-relevant document, and a topically related document excluded by that instruction, an LLM generates a complementary instruction under which the two documents exchange relevance labels. The resulting paired views directly penalize instruction-agnostic representations. At a matched 880k-example budget, replacing the non-instruction half of a Promptriever-style joint-data recipe with polarity-reversed examples improves FollowIR p-MRR from 5.27 to 8.30 (+57.5%) on a 305M-parameter encoder while increasing its instruction-conditioned Score from 20.85 to 21.38. The relative p-MRR gain repeats on a 560M-parameter backbone. The proposed recipe also improves InfoSearch nDCG@5 and shows no average degradation on four evaluated BEIR datasets with plain queries. A size-matched paraphrase control does not reproduce the instruction-sensitivity gains, supporting the conclusion that label reversal, rather than additional LLM-generated wording, provides the key supervision signal.