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

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) detection due to their broad perceptual and reasoning capabilities; however, existing MLLM-based detectors still suffer from hallucination and unstable reasoning, leading to 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 realizes three key capabilities: (1) Adaptive Instance-Conditioned Detection Strategy Planning, (2) Evidence-Centric Reasoning and Verification, and (3) Graph-Grounded Evidence Deliberation. Specifically, Hermes uses instance-conditioned retrieval-augmented generation to analyze each video and retrieve authenticity-verification knowledge for composing a tailored detection strategy. It then constructs a verifiable Evidence Reasoning Graph (ERG) to keep reasoning grounded in concrete video evidence and reduce attention drift. Finally, multi-agent deliberation audits and refines the ERG to reconcile conflicting evidence and improve reliability. With these capabilities and a library of forensic tools, Hermes enables structured, verifiable, and interpretable decision-making. Extensive experiments show that Hermes achieves state-of-the-art performance while producing auditable explanations for trustworthy video forensics.