Retrieval-Augmented Generation (RAG) systems for biomedical literature are typically evaluated using ranking metrics like Mean Reciprocal Rank (MRR), which measure how well the system identifies the single most relevant chunk. We argue that for full-text scientific documents, this paradigm is incomplete: it rewards retrieval precision while ignoring retrieval breadth -- the ability to surface evidence from across a document's structural sections. We propose GraLC-RAG, a framework that unifies late chunking with graph-aware structural intelligence, introducing structure-aware chunk boundary detection, UMLS knowledge graph infusion, and graph-guided hybrid retrieval. We evaluate six strategies on 2,359 IMRaD-filtered PubMed Central articles using 2,033 cross-section questions and two metric families: standard ranking metrics (MRR, Recall@k) and structural coverage metrics (SecCov@k, CS Recall). Our results expose a sharp divergence: content-similarity methods achieve the highest MRR (0.517) but always retrieve from a single section, while structure-aware methods retrieve from up to 15.6x more sections. Generation experiments show that KG-infused retrieval narrows the answer-quality gap to delta-F1 = 0.009 while maintaining 4.6x section diversity. These findings demonstrate that standard metrics systematically undervalue structural retrieval and that closing the multi-section synthesis gap is a key open problem for biomedical RAG.

翻译：检索增强生成（RAG）系统在生物医学文献中的评估通常采用平均倒数排名（MRR）等排序指标，这些指标衡量系统识别最相关单个文本块的能力。我们认为，对于全文科学文档而言，这一范式存在缺陷：它奖励检索精度而忽视检索广度——即从文档不同结构章节中呈现证据的能力。我们提出GraLC-RAG框架，该框架统一了延迟分块与图感知结构智能，引入了结构感知的文本块边界检测、UMLS知识图谱注入以及图引导的混合检索。我们在2,359篇经IMRaD筛选的PubMed Central文章上，使用2,033个跨章节问题及两类指标（标准排序指标MRR、Recall@k，以及结构覆盖指标SecCov@k、CS Recall）对六种策略进行了评估。实验结果揭示了显著差异：内容相似性方法取得了最高的MRR值（0.517），但始终仅从单一章节进行检索，而结构感知方法则能从多达15.6倍的章节中进行检索。生成实验表明，知识图谱注入式检索在将答案质量差距缩小至△-F1=0.009的同时，保持了4.6倍的章节多样性。这些发现表明，标准指标系统性地低估了结构检索的价值，而弥合多章节综合差距是生物医学RAG领域一项关键待解决问题。