mlsanityData to 14 Oct 2026

TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos

arXiv 2608.30704 ↗First posted 31 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perform this task reliably, a key challenge lies in accurately identifying subtle-yet-measurable unnatural artifacts. In this work, we address this challenge from a novel perspective of tool-mediated evidence discovery and propose Tool-Using Expert MLLM-based AI-generated Video Detector (TUE-Detector), a novel framework for AI-generated video detection. TUE-Detector trains a general MLLM into a task-tailored tool-using expert detector that learns to invoke suitable tools, collect concrete evidence of unnaturalness, and reason over the evidence for reliable detection. Meanwhile, TUE-Detector further introduces novel designs to equip the expert detector with high-quality and suitable tools. Extensive experiments demonstrate the effectiveness of our framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 105 references that are in the map. First posted on arXiv 31 Aug 2026.

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References · 46

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34 more
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