We study leveraging adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. Bridge documents are those that contribute to the reasoning process yet are not directly relevant to the initial query. While existing reasoning-based reranker pipelines attempt to surface these documents in ranking, they suffer from bounded recall. Naive solution with adaptive retrieval into these pipelines often leads to planning error propagation. To address this, we propose REPAIR, a framework that bridges this gap by repurposing reasoning plans as dense feedback signals for adaptive retrieval. Our key distinction is enabling mid-course correction during reranking through selective adaptive retrieval, retrieving documents that support the pivotal plan. Experimental results on reasoning-intensive retrieval and complex QA tasks demonstrate that our method outperforms existing baselines by 5.6%pt.

翻译：本研究探讨如何利用自适应检索技术确保在推理密集型检索中获取足够的"桥梁"文档。桥梁文档指那些虽不直接与初始查询相关，但能为推理过程提供支持的文档。现有的基于推理的重排序流程虽尝试在排序中呈现此类文档，但存在召回率受限的问题。若将自适应检索简单引入此类流程，常会导致规划误差传播。为解决此问题，我们提出REPAIR框架，该框架通过将推理计划重新用作自适应检索的密集反馈信号来弥合这一差距。我们的核心创新在于通过选择性自适应检索实现重排序过程中的中途修正，从而检索支持关键计划的文档。在推理密集型检索和复杂问答任务上的实验结果表明，本方法以5.6个百分点的优势超越现有基线模型。