Fine-Grained Fact-Checking for Short Videos: A Multi-Agent Report Generation Framework

With the rapid growth of short video platforms, the spread of fake news in short video format has become increasingly complex and deceptive. While existing text/image fact-checking methods fail to identify multiple fake claims within a single video, video fake news detection methods do not provide fine-grained evidence-based analysis. To address this gap, we propose a novel task: Fine-grained Fact-Checking Report Generation for Short Videos. Given a short video containing textual, visual, and audio modalities, the goal is to automatically generate a structured report that identifies specific fake claims and provides detailed analyses based on external evidence. We construct a benchmark dataset annotated by domain experts, along with fine-grained evaluation questions. Furthermore, we propose Video Multi-agent Fine-grained Fact-checking (VMFF), a training-free multi-agent framework that simulates the workflow of professional fact-checkers through three modules: (1) Video Understanding, (2) Task Decomposition, and (3) Retrieval, Reasoning, and Generation. Experimental results demonstrate its effectiveness in fine-grained fact-checking report generation.