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arXiv AI

AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning

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.

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arXiv Computer Vision
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A Survey on Industrial Anomaly Synthesis

This survey reviews industrial anomaly synthesis (IAS), a field that has been underrepresented in prior studies focused on anomaly detection. It categorizes IAS methods into four paradigms—hand‑crafted, distribution‑hypothesis, generative model, and vision‑language model—while presenting a taxonomy that facilitates systematic comparison. The paper also discusses datasets, benchmarks, evaluation metrics, multimodal advances, and practical deployment considerations, offering guidance for future research.

By Yanshu Wang, Xichen Xu, Jinbao Wang, Chengbin Ma, Xiaoning Lei, Guoyang Xie, Guannan Jiang, Zhichao Lu
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arXiv Computer Vision
Sep 3

DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation

The paper introduces DPA, a diffusion-based framework that decouples product-agnostic anomaly representations to enable zero-shot anomaly generation. By reusing real anomalies from existing source products and filtering them for plausibility, DPA learns product-irrelevant anomaly embeddings that can be transferred across products. An adaptive mask-guided pipeline and a training-free labeling module further refine the realism and localization of generated anomalies, leading to improved performance on MVTec-AD, VisA, and a new anomaly-transfer benchmark.

By Hang Yao, Yansheng Fu, Ming Liu, Zifei Yan, Yanli Ji, Hongzhi Zhang, Wangmeng Zuo
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arXiv AI
Sep 30

MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems

MAADBench is a refreshable benchmark for anomaly detection in multi‑agent systems powered by large language models. It addresses the challenge of keeping benchmarks current by sampling and coupling generative tasks, generating trace data under configurable LLM backbones, and automatically providing deterministic step‑level labels. The authors evaluated 25 anomaly‑detection methods on 5,200 labeled traces, finding that existing approaches depend heavily on supervision, struggle with subtle MAS‑specific anomalies, and lack robustness across different LLM backbones.

By Lei Ma, Dennis Hofmann, Haowen Xu, Joshua DeOliveira, Peter VanNostrand, Lei Cao, Elke Rundensteiner
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