Hermes: An Evidence-Driven Agentic Framework for Trustworthy and Explainable AI-Generated Video Detection

Abstract

Recent advances in generative video models have blurred the boundary between real and synthetic content, raising urgent concerns about digital authenticity. Multimodal large language models (MLLMs) are appealing for AI-generated video (AIGV) forensics due to their broad perceptual and reasoning capabilities; however, existing MLLM-based detectors still suffer from hallucination and unstable reasoning, yielding high false-alarm rates and generic, non-verifiable explanations. To address these issues, we propose Hermes, an evidence-driven agentic framework for trustworthy and explainable AIGV detection. Hermes is realized by three key capabilities: (1) Adaptive Instance-Conditioned Detection Strategy Planning, (2) Evidence-Centric Reasoning and Verification, and (3) Graph-Grounded Evidence Deliberation. Concretely, Hermes employs an instance-conditioned RAG mechanism to analyze each video and retrieve authenticity-verification knowledge for composing a tailored detection strategy. It then performs evidence-centric reasoning by constructing a verifiable Evidence Reasoning Graph (ERG) that maintains focus on authenticity verification and avoids attention drift or superficial reasoning. Finally, a multi-agent deliberation process audits and refines the ERG to reconcile conflicting evidence and enhance reliability. Supported by these capabilities and a rich library of internal and external forensic tools, Hermes achieves structured, verifiable, and interpretable decision-making. Extensive experiments show that Hermes delivers state-of-the-art performance while producing higher-quality, auditable explanations.