Abstract
The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable, while also maintaining details of the decision processes.
Abstract (translated)
数字平台上虚假信息的广泛传播对信息完整性构成了重大挑战。本文提出了一种多代理系统，该系统利用关系抽取技术来检测新闻文章中的虚假信息，重点分析标题和简短的文字片段。提出的“代理式AI”系统结合了四个代理：(i) 一个机器学习代理（逻辑回归），(ii) 一个维基百科知识检查代理（依赖于命名实体识别），(iii) 一个连贯性检测代理（使用大语言模型提示工程），以及(iv) 一个网页抓取数据分析器，用于提取关系三元组以进行事实核查。该系统通过“模型上下文协议”（MCP）来协调各个部分，提供共享的上下文和实时学习功能。 实验结果显示，多代理系统的整体准确率达到95.3%，F1值为0.964，显著优于单一代理和传统方法。通过计算每个代理错误分类率得出的加权聚合方法证明比算法阈值优化更有效。模块化架构使该系统易于扩展，并且能够详细记录决策过程。 这种多代理系统的提出不仅在技术上解决了虚假信息检测的问题，而且展示了如何利用不同类型的智能代理协同工作以提高系统的整体性能和准确性。
URL
https://arxiv.org/abs/2508.10143
