发表机构
Fudan University; China University of Mining and Technology - Beijing（复旦大学; 中国矿业大学（北京））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReVR提出双路径推理框架，通过智能体构建可复用验证概念并利用覆盖与查询两条路径推理，提升多模态虚假新闻检测的性能与泛化性。
AI 中文摘要
视觉语言模型（VLM）通过生成显式分析来支持多模态虚假新闻检测（FND）。近期方法通过将验证知识组织为显式概念，进一步提高了可解释性。然而，仍有两个问题有待解决：如何提高验证概念的可靠性和适用性，以及如何有效地应用可复用概念来验证未见新闻。我们提出ReVR，一种双路径推理框架，用于构建和应用可复用的验证概念以进行多模态虚假新闻检测。一个智能体工作流对候选概念进行 grounding 和整合，而统计画像则刻画其历史行为。在推理阶段，覆盖导向路径使用可训练编码器从完整概念库中聚合证据，而查询聚焦路径则提示冻结的VLM对所选概念及其观察进行推理。当两条路径的预测不一致时，一个学习到的冲突解决器会在两者之间进行选择。在虚假新闻基准上的实验证明了该方法在检测性能和泛化性方面的有效性。
英文摘要
Vision-language models (VLMs) support multimodal fake news detection (FND) by producing explicit analyses. Recent methods further improve interpretability by organizing verification knowledge into explicit concepts. However, two questions remain: how to improve the reliability and applicability of verification concepts, and how to effectively apply reusable concepts to verify unseen news. We propose \textbf{ReVR}, a dual-path reasoning framework that constructs and applies reusable verification concepts for multimodal fake news detection. An agentic workflow grounds and consolidates candidate concepts, while statistical profiles characterize their historical behavior. During inference, a coverage-oriented path aggregates evidence from the complete concept library using a trainable encoder, while a query-focused path prompts a frozen VLM to reason over selected concepts and their observations. A learned conflict resolver selects between the two predictions when they disagree. Experiments on fake news benchmarks demonstrate the effectiveness of the method regarding detection performance and generalizability.
