IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2608.29783v1 Announce Type: new Abstract: Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature di...
AnomalyAgent is an agentic framework for industrial anomaly synthesis that uses five tools—Prompt Generation, Image Generation, Quality Evaluation, Knowledge Retrieval, and Mask Generation—to iteratively refine anomaly samples. It employs a two‑stage training process combining supervised fine‑tuning and reinforcement learning, guided by a three‑part reward system that encourages task performance, self‑reflection, and trajectory adherence. On the MVTec‑AD dataset, AnomalyAgent outperforms zero‑shot state‑of‑the‑art methods, achieving superior anomaly generation scores and high classification and detection accuracy.
arXiv:2608. 08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios.
arXiv:2610.15032v1 Announce Type: cross Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in whi...
The paper investigates whether in‑domain training alone can enable multimodal large language models (MLLMs) to perform fine‑grained industrial anomaly understanding (MM‑IAU). It shows that even specialized MLLMs fail to match human experts on defect localization and detection, and that combining different MLLMs does not eliminate this shortfall. To address this, the authors propose SiGMA, a spatially grounded multi‑agent framework that splits tasks among heterogeneous MLLM agents and a dedicated visual defect expert, achieving 85.2% accuracy on the MMAD benchmark—close to human performance and surpassing the best trained specialist.
arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.