Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success, most existing MRAG frameworks treat retrieved evidence as indivisible documents, implicitly assuming that all content within a document is equally informative. In practice, however, sometimes only a small fraction of a document is relevant to a given query, while the remaining content introduces substantial noise that may lead to performance degradation. We address this fundamental limitation by reframing MRAG as a fine-grained evidence selection problem. We propose Fragment-level Evidence Selection for RAG (FES-RAG), a framework that selects atomic multimodal fragments rather than entire documents as grounding evidence. FES-RAG decomposes retrieved multimodal documents into sentence-level textual fragments and region-level visual fragments, enabling precise identification of evidence that directly supports generation. To guide fragment selection, we introduce Fragment Information Gain (FIG), a principled metric that measures the marginal contribution of each fragment to the MLLM's generation confidence. Based on FIG, we distill fragment-level utility judgments from a high-capacity MLLM into a lightweight selector, achieving accurate evidence selection with low inference overhead. Experiments on the M2RAG benchmark show that FES-RAG consistently outperforms state-of-the-art document-level MRAG methods, achieving up to 27 percent relative improvement in CIDEr. By selecting fewer yet more informative fragments, our approach substantially reduces context length while improving factual accuracy and generation coherence.

翻译：多模态检索增强生成（MRAG）被广泛用于多模态大语言模型（MLLMs）中，通过引入外部证据来减少幻觉。尽管取得了成功，但现有大多数MRAG框架将检索到的证据视为不可分割的文档，隐含地假设文档中的所有内容具有同等信息量。然而在实践中，通常只有文档的一小部分与给定查询相关，而其余内容会引入大量噪声，可能导致性能下降。我们通过将MRAG重新定义为细粒度证据选择问题来解决这一根本局限。本文提出面向RAG的片段级证据选择（FES-RAG），该框架选择原子多模态片段而非完整文档作为基础证据。FES-RAG将检索到的多模态文档分解为句子级文本片段和区域级视觉片段，从而能够精确识别直接支持生成的证据。为引导片段选择，我们引入片段信息增益（FIG）这一原则性度量指标，用于衡量每个片段对MLLM生成置信度的边际贡献。基于FIG，我们将高容量MLLM的片段级效用判断蒸馏至轻量级选择器，在低推理开销下实现精准证据选择。在M2RAG基准上的实验表明，FES-RAG持续优于最先进的文档级MRAG方法，在CIDEr指标上实现高达27%的相对提升。通过选择更少但更具信息量的片段，我们的方法在提升事实准确性和生成连贯性的同时，显著缩短了上下文长度。