M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2603.00055v1
• Date: Tue, 10 Feb 2026 07:20:19 GMT
• Title: M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
• Abstract summary: M3-AD is a unified reflection-aware framework for industrial anomaly detection.<n> RA-Monitor guides models to perform controlled self-correction when initial judgments are unreliable.
• Score: 87.27067827385599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable decisions in fine-grained and structurally complex industrial scenarios, and lack effective self-corrective mechanisms. To address this issue, we propose M3-AD, a unified reflection-aware multimodal framework for industrial anomaly detection. M3-AD comprises two complementary data resources: M3-AD-FT, designed for reflection-aligned fine-tuning, and M3-AD-Bench, designed for systematic cross-category evaluation, together providing a foundation for reflection-aware learning and reliability assessment. Building upon this foundation, we propose RA-Monitor, which models reflection as a learnable decision revision process and guides models to perform controlled self-correction when initial judgments are unreliable, thereby improving decision robustness. Extensive experiments conducted on M3-AD-Bench demonstrate that RA-Monitor outperforms multiple open-source and commercial MLLMs in zero-shot anomaly detection and anomaly analysis tasks. Code will be released at https://github.com/Yanhui-Lee/M3-AD.

Related papers

• RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants [48.63201300674347]
  Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories.<n>MSLMs offer a deployable alternative, yet progress is constrained by the lack of comprehensive robustness analyses.<n>We develop RobustMAD to evaluate model robustness through diverse open-ended queries.
  arXiv  Detail & Related papers  (2026-06-26T09:15:07Z)
• Text-Guided Multimodal Unified Industrial Anomaly Detection [71.95719669933312]
  We propose a unified multimodal industrial anomaly detection framework guided by text semantics.<n>The framework consists of two core modules: a Geometry-Aware Cross-Modal Mapper and an Object-Conditioned Textual Feature Adaptor.<n>Our method achieves state-of-the-art performance in classification and localization under unsupervised settings.
  arXiv  Detail & Related papers  (2026-04-24T13:21:22Z)
• Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time Scaling [41.61826091940538]
  Vision-language models (VLMs) have been proven effective for detecting multi-modal misinformation on social platforms.<n>However, a single VLM's capacity falls short in the more complex mixed-source multi-modal misinformation detection task.<n>We present AgentM3D, a multi-agent framework for zero-shot misinformation detection.
  arXiv  Detail & Related papers  (2026-03-03T02:07:52Z)
• Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method [96.63801368613177]
  We present a new task that elevates video anomaly analysis from descriptive understanding to structured, multi-stage reasoning.<n>We present a new dataset with 8,641 videos, totaling more than 50,000 samples, making it one of the largest datasets for video anomaly understanding.<n>Building upon the proposed task and dataset, we develop an end-to-end MLLM-based VAR model termed Vad-R1-Plus, which supports adaptive hierarchical reasoning and risk-aware decision making.
  arXiv  Detail & Related papers  (2026-01-15T08:09:04Z)
• AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection [40.34270276536052]
  Industrial Anomaly Detection (IAD) poses a formidable challenge due to the scarcity of defective samples.<n>Traditional approaches, often constrained by hand-crafted features or domain-specific expert models, struggle to address this limitation.<n>We introduce AnomalyR1, a pioneering framework that leverages VLM-R1, a Multimodal Large Language Model (MLLM) renowned for its exceptional generalization and interpretability.
  arXiv  Detail & Related papers  (2025-04-16T09:48:41Z)
• Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process [67.99194145865165]
  We modify the AnyRes structure of the LLaVA model to provide the potential anomalous areas identified by existing IAD models to the LMMs.<n>Considering that the generation of defects is closely related to the manufacturing process, we propose a manufacturing-driven IAD paradigm.<n>We present Triad, a novel LMM-based method incorporating an expert-guided region-of-interest tokenizer and manufacturing process.
  arXiv  Detail & Related papers  (2025-03-17T13:56:57Z)
• Rethinking Multi-Modal Object Detection from the Perspective of Mono-Modality Feature Learning [18.268054258939213]
  Multi-Modal Object Detection (MMOD) has been widely applied in various applications.<n>This paper introduces linear probing evaluation to the multi-modal detectors.<n>We construct a novel framework called M$2$D-LIF, which consists of the Mono-Modality Distillation (M$2$D) method and the Local Illumination-aware Fusion (LIF) module.
  arXiv  Detail & Related papers  (2025-03-14T18:15:53Z)
• Robust Modality-incomplete Anomaly Detection: A Modality-instructive Framework with Benchmark [69.02666229531322]
  We introduce a pioneering study that investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD)<n>We find that most existing MIAD methods perform poorly on the MIIAD Bench, leading to significant performance degradation.<n>We propose a novel two-stage Robust modAlity-aware fusing and Detecting framewoRk, abbreviated as RADAR.
  arXiv  Detail & Related papers  (2024-10-02T16:47:55Z)
• PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain [37.448177723993346]
  We present PCA-Bench, a benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs) Given task instructions and diverse contexts, the model is required to seamlessly integrate Perception, Cognition, and Action in a reasoning chain. We propose PCA-Eval, an automatic evaluation protocol, and assess 10 prevalent MLLMs.
  arXiv  Detail & Related papers  (2024-02-21T07:09:58Z)
• Multimodal Industrial Anomaly Detection via Hybrid Fusion [59.16333340582885]
  We propose a novel multimodal anomaly detection method with hybrid fusion scheme. Our model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTecD-3 AD dataset.
  arXiv  Detail & Related papers  (2023-03-01T15:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.