RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserves explicit citation provenance by constructing a bank of Q&A nuggets from retrieved documents and uses them to guide extraction, selection, and report generation. Reasoning on nuggets avoids repeated information through clear and interpretable Q&A semantics - instead of opaque cluster abstractions - while maintaining citation provenance throughout the entire generation process. Evaluated on the TREC NeuCLIR 2024 collection, our Crucible system substantially outperforms Ginger, a recent nugget-based RAG system, in nugget recall, density, and citation grounding.

翻译：RAGE系统将自动评估（E）的思想融入检索增强生成（RAG）框架。作为实例，我们提出Crucible——一种基于要点增强的生成系统，通过从检索文档中构建问答要点库来保留显式引文溯源，并以此指导信息提取、筛选和报告生成。基于要点进行推理，可通过清晰且可解释的问答语义避免重复信息——摒弃不透明的聚类抽象——同时在整个生成过程中维持引文溯源。在TREC NeuCLIR 2024数据集上的评估表明，我们的Crucible系统在要点召回率、密度和引文依据性方面显著优于近期基于要点的RAG系统Ginger。