Retrieval-augmented generation (RAG) for multi-hop question answering is limited by retrieving semantically redundant documents, when reasoning instead needs diverse, complementary sources. We introduce Vendi-RAG, which treats set-level semantic diversity as a first-class retrieval objective. It combines an eigenvalue-based diversity score (the Vendi Score) with query relevance into a single Vendi Retrieval Score, embedded in an iterative loop that uses an LLM-based answer-quality signal to adaptively rebalance diversity and relevance. On HotpotQA, MuSiQue, and 2WikiMultiHopQA, Vendi-RAG improves accuracy by +2.4, +1.6, and +2.4 points over the strongest diversity-aware iterative baseline (Adaptive Retrieval + MMR). A fixed-s variant captures most of the gain at no extra inference cost, while the LLM-judge loop adds a smaller but significant gain on hard queries. Results hold across five instruction-tuned backbones and two dense retrievers, identifying set-level retrieval diversity as a primary, controllable axis for improving multi-hop RAG.

Large Language Models (LLMs) have greatly advanced medical Question Answering (QA) by leveraging vast clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources can undermine the reliability of these systems. We address this challenge with Agentic Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graph (MKG), integrates reasoning, and retrieves current external evidence from the MKG for medical QA.Evaluations on the MEDQA and MEDMCQA benchmarks demonstrate the effectiveness of AMG-RAG, achieving an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on MEDMCQA—surpassing both comparable models and those 10 to 100 times larger. By dynamically linking new findings and complex medical concepts, AMG-RAG not only boosts accuracy but also enhances interpretability for medical queries, which has a critical impact on delivering up-to-date, trustworthy medical insights.