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Computer Science > Artificial Intelligence arXiv:2608.

11260 (cs) [Submitted on 7 Aug 2026] Title:Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning Authors:Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang View a PDF of the paper titled Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning, by Shibo Gao and 3 other authors View PDF HTML (experimental) Abstract:Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.

Existing approaches exhibit a "when-what" dissociation: traditional DNN-based methods localize when anomalies occur but lack semantic understanding, whereas LLM-based methods explain what happens but neglect precise temporal grounding.We attribute this to the absence of a unified reasoning paradigm.

Inspired by how humans inspect surveillance videos - glancing globally to form temporal hypotheses, scrutinizing suspicious segments, and thinking iteratively to correct errors - we study this global-to-local paradigm from two perspectives.

We first propose Glance then Scrutinize (GtS), a training-free framework using static and dynamic textual guidance for coarse-to-fine anomaly grounding and understanding, balancing accuracy and speed.

To break the ceiling imposed by frozen external modules, we further propose a tool-augmented agentic VAD method, where a multimodal large language model learns to invoke a video cropping tool, inspect densely resampled frames, and self-correct mislocalized hypotheses, via cold-start supervised fine-tuning followed by reinforcement learning with a joint answer-grounding reward.

For training and evaluation, we extend our prior VAGU benchmark into VAGU-T (Video Anomaly Grounding, Understanding, and Thinking), comprising 7,567 real-world videos over 21 anomaly categories with human-validated grounding, explanations, QA pairs, and chain-of-thought tool-calling traces.

We further introduce JeAUG, a metric jointly evaluating semantic interpretability and temporal precision.Experiments show that GtS substantially surpasses training-free baselines, while the agentic model delivers both higher accuracy and faster inference.Comments: 34 pages, 8 figures, 8 tables.

Journal extension of our AAAI 2026 paper (arXiv:2507.21507) Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.2.10; I.4.8; I.2.7 Cite as: arXiv:2608.11260 [cs.AI] (or arXiv:2608.11260v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.

2608.

11260 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shibo Gao [view email] [v1] Fri, 7 Aug 2026 17:07:49 UTC (13,112 KB) Full-text links: Access Paper: View a PDF of the paper titled Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning, by Shibo Gao and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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