Multi-turn Physics-informed Vision-language Model for Physics-grounded Anomaly Detection

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• The proposed physics-informed instruction tuning framework achieves 96.7% AUROC in video-level anomaly detection on the Phys-AD benchmark.result 95%
• The method substantially outperforms prior state-of-the-art approaches which achieved 66.9% AUROC.result 90%
• The framework yields superior causal explanations with a 0.777 LLM score.result 90%
• Existing VLMs fail to capture kinematic constraints, leading to poor performance on anomalies involving irregular rotations or violated mechanical motions.limitation 85%
• The method explicitly encodes object properties, motion paradigms, and dynamic constraints into structured prompts.method 90%
• The approach uses multi-turn dialogues to decompose causal reasoning into incremental steps.method 85%
• The method requires high-quality video data and precise physical priors for training.limitation 80%
• The approach may struggle in highly variable or unstructured environments.limitation 75%

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