A critical vulnerability in Retrieval-Augmented Generation (RAG) systems, often overlooked in standard performance evaluations, has been brought to light through a reproducible demonstration. The core issue lies not in the retrieval of relevant documents, but in the subsequent assembly and processing of that information, a phase where conflicting data can lead to confidently incorrect answers […]