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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# M3-AD: RA-Monitor
+
+This repository contains the model weights for **RA-Monitor**, a unified reflection-aware multimodal framework for industrial anomaly detection. RA-Monitor is part of the M3-AD framework presented in the paper [M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection](https://huggingface.co/papers/2603.00055).
+
+## Model Description
+RA-Monitor addresses the issue where multimodal large language models (MLLMs) produce high-confidence but unreliable decisions in complex industrial scenarios. It introduces a reflection-aware mechanism that models reflection as a learnable decision revision process. This allows the model to perform controlled self-correction when initial judgments are unreliable, significantly improving anomaly type recognition and spatial localization.
+
+The framework is built upon:
+- **RA-Monitor**: A mechanism that equips pre-trained models with thinking and reflective abilities.
+- **M3-AD-FT**: A dataset designed for reflection-aligned fine-tuning.
+- **M3-AD-Bench**: A benchmark for systematic cross-category evaluation of industrial anomaly detection.
+
+## Resources
+- **Paper:** [M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection](https://huggingface.co/papers/2603.00055)
+- **GitHub Repository:** [Yanhui-Lee/M3-AD](https://github.com/Yanhui-Lee/M3-AD)
+
+## Citation
+If you find this work useful, please cite the following paper:
+```bibtex
+@article{m3ad2026,
+  title={M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection},
+  author={Li, Yanhui and others},
+  journal={arXiv preprint arXiv:2603.00055},
+  year={2026}
+}
+```