Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a multimodal vision-language framework for zero-shot grounded industrial anomaly detection. Given RGB images, sensor images, and 3D point clouds, ZSG-IAD generates structured anomaly reports and pixel-level anomaly masks. ZSG-IAD introduces a language-guided two-hop grounding module: (1) anomaly-related sentences select evidence-like latent slots distilled from multimodal features, yielding coarse spatial support; (2) selected slots modulate feature maps via channel-spatial gating and a lightweight decoder to produce fine-grained masks. To improve reliability, we further apply Executable-Rule GRPO with verifiable rewards to promote structured outputs, anomaly-region consistency, and reasoning-conclusion coherence. Experiments across multiple industrial anomaly benchmarks show strong zero-shot performance and more transparent, physically grounded explanations than prior methods. We will release code and annotations to support future research on trustworthy industrial anomaly detection systems.
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；University of Surrey（萨里大学）；Tencent（腾讯）；Bielefeld University（比勒费尔德大学）
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
University of Science and Technology of China（中国科学技术大学）；Northeastern University（东北大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection
CNR-ISASI, Institute of Applied Sciences and Intelligent Systems / National Research Council of Italy（CNR-ISASI应用科学与智能系统研究所/意大利国家研究理事会）；CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing（CNR-STIIMA智能工业技术与系统先进制造研究所）；Institute d'Electronique de Microelectronique et de Nanotechnologie (IEMN) / Universite Polytechnique Hauts de France（微电子与纳米技术研究所（IEMN）/法国高等法国理工学院）
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院）；Santa Clara University（圣克拉拉大学）；LongCat Team（LongCat团队）；Independent Researcher（独立研究者）；New York University（纽约大学）；Sun Yat-sen University（孙中山大学）；Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
Fudan University（复旦大学）；Youtu Lab, Tencent（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；Rongcheer Co., Ltd（融雪科技有限公司）；City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）；Shanghai Ocean University（上海海洋大学）
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
Faculty of Computing, Harbin Institute of Technology, Harbin, China University of New South Wales, Sydney, Australia School of Biomedical Engineering, Tsinghua University, Beijing, China School of Artificial Intelligence；Robotics, Hunan University, Changsha, China Big data institute, Central South University, Sydney, Australia DZ-Matrix, Beijing, China Chongqing Research Institute of HIT, Chongqing, China
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning
School of Computer Science and Technology, Guangdong University of Technology（广东技术大学计算机科学与技术学院）；School of Computer Science and Intelligence Education, Lingnan Normal University（岭南师范学院计算机科学与智能教育学院）
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Southern University of Science and Technology（南方科技大学）；University of Surrey（萨里大学）；Tencent Youtu Lab（腾讯优图实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Westlake University（西湖大学）
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（大亚湾大学计算机与信息科技学院）；College of Computer Science, Nankai University（南开大学计算机学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）；National Engineering Laboratory of Big Data System Computing Technology, Shenzhen University（大数据系统计算技术国家工程实验室）
SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection
Institute of Artificial Intelligence, University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学人工智能研究所）；Human-Machine Interaction Laboratory, University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学人机交互实验室）；Applied AI Lab, Phenikaa School of Computing, Phenikaa University（费尼卡大学计算学院应用人工智能实验室）
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；Macau University of Science and Technology（澳门科技大学）；Tencent YouTu Lab（腾讯YouTu实验室）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）；Centre for Frontier AI Research, A*STAR（前沿人工智能研究中心，A*STAR）
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Department of Artificial Intelligence, FPT University（FPT大学人工智能系）；Department of IT, FPT University（FPT大学信息技术系）；Department of Computing Fundamental, FPT University（FPT大学计算基础系）
NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Sony Computer Science Laboratories (Sony CSL)（索尼计算机科学实验室）；The University of Tokyo（东京大学）；National Taiwan Normal University (NTNU)（国立台湾师范大学（NTNU））
GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment
National Taiwan University（台湾大学）；National Taiwan Normal University（台湾师范大学）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究院）；Microsoft Taiwan Corporation（微软台湾公司）；VinUniversity；National Taiwan University of Science and Technology（台湾科技大学）
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Mechanical Science & Engineering, Huazhong University of Science & Technology（华中科技大学机械科学与工程学院）
Search is All You Need for Few-shot Anomaly Detection
Academy for Engineering and Technology Fudan University（复旦大学工程与技术学院）；School of Biomedical Engineering Tsinghua University（清华大学生物医学工程学院）；School of Computer Science Fudan university（复旦大学计算机学院）；College of Design & Innovation Tongji University（同济大学设计与创新学院）；School of Physics and Mechanical & Electrical Engineering Hexi University（河西大学物理与机械电子工程学院）；Engineering Research Center of AI&Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）；Academy for Engineering&Technology Fudan university（复旦大学工程与技术学院）
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密测量技术与仪器国家重点实验室，精密仪器系）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备技术国家重点实验室）；Hunan University School of Robotics（湖南大学机器人学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
