Multi-condition retrieval requires systems to identify documents that satisfy multiple distinct constraints, moving beyond mere topical relevance. While query decomposition is widely adopted as an intuitive remedy, its effectiveness across different retrieval pipeline stages remains underexplored. In this paper, we conduct a stage-aware empirical study and uncover a stark, stage-dependent effect: decomposition during initial retrieval frequently harms retrieval performance due to semantic dilution, yet substantially improves reranking by enabling more fine-grained constraint verification. Motivated by these insights, we propose a principled Stage-Aware Decomposition framework that retains the monolithic query during initial retrieval to preserve global semantic context, while employing sub-queries exclusively during reranking for fine-grained constraint matching. Extensive evaluations on the MultiConIR and SSRB benchmarks demonstrate that our framework consistently improves ranking performance for compositional queries across multiple retrieval and reranking models. We release our code at https://github.com/EIT-NLP/Query-Decompose.

翻译：多条件检索要求系统识别同时满足多个不同约束条件的文档，这超越了单纯的主题相关性。尽管查询分解作为一种直观的解决方案被广泛采用，但其在检索管线的不同阶段中的有效性尚未得到充分探索。本文通过阶段感知的实证研究，揭示了一个显著的阶段依赖性现象：在初始检索阶段进行分解常因语义稀释而损害检索性能，但在重排序阶段却能通过实现更细粒度的约束验证显著提升效果。基于这些发现，我们提出了一种原则性的阶段感知分解框架，该框架在初始检索阶段保留整体查询以维持全局语义上下文，仅在重排序阶段使用子查询进行细粒度约束匹配。在MultiConIR和SSRB基准上的广泛评估表明，我们的框架在多种检索与重排序模型上持续提升组合查询的排序性能。我们已在https://github.com/EIT-NLP/Query-Decompose 发布代码。