标题： MIRAGE：具有网络基础推理的多模态虚假信息检测代理框架 显示英文标题

标题： MIRAGE: Agentic Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning

摘要： 错误信息通过每天数十亿条结合文本和图像的多模态帖子在网络平台上传播，超出了人工事实核查的能力。 监督检测模型需要特定领域的训练数据，并且无法在各种篡改策略中进行泛化。 我们提出了MIRAGE，这是一个推理时、可插拔模型的代理框架，将多模态验证分解为四个顺序模块：视觉真实性评估检测AI生成的图像，跨模态一致性分析识别上下文不当的重新使用，检索增强的事实检查通过迭代问题生成将声明与网络证据联系起来，以及一个校准的判断模块整合所有信号。 MIRAGE将视觉-语言模型推理与有针对性的网络检索相结合，输出结构化且带有引用链接的推理过程。 在MMFakeBench验证集（1000个样本）上， 使用GPT-4o-mini的MIRAGE实现了81.65%的F1值和75.1%的准确率，比最强的零样本基线（GPT-4V与MMD-Agent在74.0% F1值）高出7.65点，同时保持34.3%的误报率，而仅依靠判断者的基线则为97.3%。 测试集结果（5000个样本）确认了泛化能力，达到81.44%的F1值和75.08%的准确率。 消融研究显示，视觉验证贡献了5.18的F1值，检索增强的推理贡献了2.97的F1值。 我们的结果表明，通过网络检索的分解代理推理可以达到监督检测器的性能，而无需特定领域的训练，从而在标记数据仍然稀缺的多模态中实现错误信息检测。 显示英文摘要

摘要： Misinformation spreads across web platforms through billions of daily multimodal posts that combine text and images, overwhelming manual fact-checking capacity. Supervised detection models require domain-specific training data and fail to generalize across diverse manipulation tactics. We present MIRAGE, an inference-time, model-pluggable agentic framework that decomposes multimodal verification into four sequential modules: visual veracity assessment detects AI-generated images, cross-modal consistency analysis identifies out-of-context repurposing, retrieval-augmented factual checking grounds claims in web evidence through iterative question generation, and a calibrated judgment module integrates all signals. MIRAGE orchestrates vision-language model reasoning with targeted web retrieval, outputs structured and citation-linked rationales. On MMFakeBench validation set (1,000 samples), MIRAGE with GPT-4o-mini achieves 81.65% F1 and 75.1% accuracy, outperforming the strongest zero-shot baseline (GPT-4V with MMD-Agent at 74.0% F1) by 7.65 points while maintaining 34.3% false positive rate versus 97.3% for a judge-only baseline. Test set results (5,000 samples) confirm generalization with 81.44% F1 and 75.08% accuracy. Ablation studies show visual verification contributes 5.18 F1 points and retrieval-augmented reasoning contributes 2.97 points. Our results demonstrate that decomposed agentic reasoning with web retrieval can match supervised detector performance without domain-specific training, enabling misinformation detection across modalities where labeled data remains scarce.