ARES: Autonomous Real-time Evaluation System

In an era where misinformation poses a strategic threat to public safety, democratic processes, and global stability, traditional fact- checking and detection methods fall short—lacking speed, scalability, and contextual depth. This paper presents ARES (Autonomous Real-time Evaluation System), an AI-driven, multi-modal misinformation detection and threat intelligence framework. Unlike existing systems limited to static text analysis or isolated classifiers, ARES integrates real-time processing of text, images, audio, video, and URLs with geopolitical inference, behavioral analytics, and symbolic reasoning. Its architecture features a triple-layered verification engine, deepfake and voice tampering detection, semantic drift tracking, and adversarial robustness, all tied into an actionable dashboard for analysts. Through comprehensive benchmarking, ARES demonstrates superior accuracy (F1-score: 0.92), low-latency performance (415ms/claim), and high resilience under stress scenarios such as election interference and health infodemics. Designed for deployment in national security, disaster response, and information warfare settings, ARES marks a paradigm shift toward scalable, autonomous digital truth infrastructure.