AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection

2026-05-28 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

AnomalyAgent is a novel training-free, agentic framework designed for zero-shot and few-shot anomaly detection, utilizing the advanced reasoning and generalization capabilities of multimodal large language models (MLLMs). This approach addresses limitations in existing vision-language model (VLM)-based methods, which often require extensive training on auxiliary datasets and lack deep contextual understanding for complex anomalies. AnomalyAgent integrates a comprehensive anomaly-centric toolset, enabling adaptive MLLM-driven reasoning in zero-shot scenarios, alongside a customized memory module that grounds anomaly reasoning with few-shot, in-context reference examples. Evaluated beyond simple surface defects and lesions, the framework demonstrates superior performance in detecting diverse logical and contextual anomalies within logistics and manufacturing settings. Extensive experiments show AnomalyAgent substantially outperforms training-free VLM-based AD and generic agentic methods, highlighting its strong generalization across both zero-shot and few-shot detection tasks.

Key takeaway

For Machine Learning Engineers developing anomaly detection systems, AnomalyAgent offers a compelling alternative to traditional VLM-based methods. You should consider integrating MLLM-driven agentic frameworks to tackle complex, contextual anomalies without requiring extensive auxiliary dataset training. This approach enhances generalization in both zero-shot and few-shot scenarios, potentially streamlining deployment and improving detection accuracy for critical applications like manufacturing quality control or logistics monitoring.

Key insights

AnomalyAgent uses training-free agentic MLLMs for advanced zero-shot and few-shot anomaly detection, surpassing VLM limitations.

Principles

• MLLMs provide superior reasoning for complex anomaly detection.
• Agentic frameworks enable adaptive, context-aware anomaly reasoning.

Method

AnomalyAgent integrates an anomaly-centric toolset for MLLM-driven zero-shot reasoning and a customized memory module to ground few-shot anomaly detection with in-context examples.

In practice

• Identify logical and contextual anomalies in industrial settings.
• Implement MLLM-based anomaly detection without auxiliary dataset training.

Topics

• AnomalyAgent
• Anomaly Detection
• Multimodal LLMs
• Zero-shot Learning
• Few-shot Learning
• Agentic AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.