Beyond Relevance: Broad-to-Deep Contextual Evidence Reasoning for Retrieval-Augmented Medical Question Answering

Medical question answering increasingly relies on retrieval-augmented generation to provide large language models with external evidence, yet most existing methods still treat evidence construction as a largely static relevance-ranking problem. Inspired by how people often approach unfamiliar problems, first surveying a broad range of potentially useful references, then organizing the most informative evidence, and finally following concepts that remain unclear, we formulate evidence acquisition as a broad-to-deep process. Towards this end, we propose a novel approach named Broad-to-deep Contextual Evidence Reasoning (BRACE) for medical question answering with two complementary stages. The broad stage constructs a compact evidence context through determinantal resolution packing, where candidate documents are assessed by both their relevance to the question and the uncertainty they resolve, while the determinantal objective preserves complementary information across the selected set. Building on this context, the deep stage performs progressive uncertainty resolution by iteratively localizing high-uncertainty concepts within the evolving question and evidence context and converting them into targeted retrieval cues. Newly acquired evidence reshapes the context and may expose more specific information needs, allowing retrieval to progressively move from broad semantic coverage toward deeper supporting knowledge. In this way, BRACE lets the evolving context determine both which evidence should be retained and where subsequent retrieval should proceed. Experiments on five medical question-answering benchmarks with three open-source readers consistently demonstrate the effectiveness of our framework over strong retrieval, generation, and hybrid knowledge-augmentation baselines.