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）的思想整合到检索增强生成（RAG）中。作为一个具体实例，我们提出了Crucible，这是一个片段增强生成系统，它通过从检索到的文档构建问答片段库来保留明确的引用来源，并利用这些片段指导信息提取、选择和报告生成。在片段上进行推理，通过清晰且可解释的问答语义——而非不透明的聚类抽象——避免了信息重复，同时在生成全过程中保持了引用来源的可追溯性。在TREC NeuCLIR 2024数据集上的评估表明，我们的Crucible系统在片段召回率、密度和引用定位方面显著优于近期基于片段的RAG系统Ginger。