发表机构
Beijing Institute of Fashion Technology; Shenyang University of Technology（北京服装学院; 沈阳工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对零样本假新闻检测的现有方法无法识别重复虚假手段与跨模态差异的问题，本文提出多模态检索增强框架MRAFnd，经多智能体协作推理，在Weibo-21等数据集上准确率最高提升2.35%，优于现有方法。
AI 中文摘要
多模态内容的快速传播加剧了虚假新闻的扩散，对社会诚信构成重大威胁。当前检测系统面临的一项严峻挑战是在零样本场景下识别与新事件相关的错误信息。主流零样本方法通常通过语义匹配孤立地评估新闻条目，这种策略无法识别过往宣传活动中重复使用的虚假信息手段，也缺乏识别细微跨模态差异所需的复杂推理能力。为克服这些缺陷，我们提出MRAFnd，这是一种面向零样本假新闻检测的新型多模态检索增强框架。MRAFnd模拟分析师协作团队验证新闻真实性：框架首先通过基于多模态相似度的新闻检索，从未标注参考数据库中收集上下文相似的文章语料库；随后在分叉证据推理阶段，智能体执行双向分析，从检索到的证据中提取关键模式；最后，涉及分析师和仲裁智能体的多智能体协作辩论，通过结构化论述得出明确且可靠的结论。在三个基准数据集上的综合实验表明，MRAFnd显著优于最先进的基线方法，在要求较高的Weibo-21数据集上实现了高达2.35%的准确率提升。
英文摘要
The rapid dissemination of multimodal content has intensified the spread of fabricated news, presenting a substantial threat to social integrity. A formidable challenge for current detection systems is identifying misinformation related to novel events in zero-shot scenarios. Prevailing zero-shot methods typically assess news items in isolation via semantic matching, a strategy that fails to recognize the recycled disinformation tactics from past campaigns and lacks the sophisticated reasoning needed to identify subtle, cross-modal discrepancies. To surmount these deficiencies, we introduce \textbf{MRAFnd}, a novel \underline{\textbf{M}}ultimodal \underline{\textbf{R}}etrieval-\underline{\textbf{A}}ugmented Framework for Zero-Shot \underline{\textbf{F}}ake \underline{\textbf{N}}ews \underline{\textbf{D}}etection. MRAFnd emulates a collaborative team of analysts to verify news veracity. The framework initiates with \textbf{Multimodal Similarity-based News Retrieval} to assemble a corpus of contextually analogous articles from an unlabeled reference database. Subsequently, during the \textbf{Bifurcated Evidential Reasoning} stage, agents perform a dual-directional analysis to extract critical patterns from the retrieved evidence. Finally, a \textbf{Multi-Agent Collaborative Debate}, involving Analyst and Arbiter agents, engages in a structured discourse to arrive at a definitive and robust conclusion. Comprehensive experiments on three benchmark datasets reveal that MRAFnd markedly surpasses state-of-the-art baselines, achieving an accuracy gain of up to 2.35\% on the demanding Weibo-21 dataset.
Comments14 pages, 6 figures, 2 tables, Presented at the MMM 2026
