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：我们研究利用自适应检索确保在推理密集型检索中获取充足的"桥梁"文档。桥梁文档是指那些参与推理过程但初始查询并不直接相关的文档。现有基于推理的重排序流程虽试图在排序中呈现这些文档，但受限于召回率瓶颈。若简单将自适应检索引入这些流程，往往会导致规划错误传播。为解决此问题，我们提出REPAIR框架，通过将推理计划重塑为用于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，主动检索支撑关键推理计划的文档。在推理密集型检索与复杂问答任务上的实验结果表明，该方法相较现有基线模型提升了5.6%的百分点。