# IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection

arXiv: https://arxiv.org/abs/2508.09178 · HTML: https://mlsanity.com/paper/2508.09178/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model](https://mlsanity.com/paper/2504.07615/) (2025)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection](https://mlsanity.com/paper/2410.09453/) (2024)
- [Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection](https://mlsanity.com/paper/2310.19070/) (2023)
- [AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.11914/) (2025)
- [Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?](https://mlsanity.com/paper/2501.15795/) (2025)
- [Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization](https://mlsanity.com/paper/2308.15939/) (2023)
- [LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions](https://mlsanity.com/paper/2503.20252/) (2025)
- [LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection](https://mlsanity.com/paper/2504.12749/) (2025)
- [LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning](https://mlsanity.com/paper/2504.19524/) (2025)
- [Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process](https://mlsanity.com/paper/2503.13184/) (2025)

## Built on by (most cited first, 17 of 17)

- [Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning](https://mlsanity.com/paper/2602.09850/) (2026)
- [AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison](https://mlsanity.com/paper/2603.13779/) (2026)
- [AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation](https://mlsanity.com/paper/2512.13671/) (2025)
- [Learning to Watch: Active Video Anomaly Understanding via Interleaved Policy Optimization](https://mlsanity.com/paper/2607.00622/) (2026)
- [Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models](https://mlsanity.com/paper/2604.07802/) (2026)
- [EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models](https://mlsanity.com/paper/2602.17419/) (2026)
- [ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection](https://mlsanity.com/paper/2511.22436/) (2025)
- [M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection](https://mlsanity.com/paper/2603.00055/) (2026)
- [AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models](https://mlsanity.com/paper/2603.01305/) (2026)
- [WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning](https://mlsanity.com/paper/2604.27629/) (2026)
- [CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection](https://mlsanity.com/paper/2607.02930/) (2026)
- [OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation](https://mlsanity.com/paper/2607.18850/) (2026)
- [VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2609.34949/) (2026)
- [AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation](https://mlsanity.com/paper/2605.10397/) (2026)
- [Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection](https://mlsanity.com/paper/2607.03817/) (2026)
- [ADOPD: Reference-Privileged On-Policy Distillation for MLLM-Based Industrial Anomaly Detection](https://mlsanity.com/paper/2608.09789/) (2026)
- [Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space](https://mlsanity.com/paper/2609.29457/) (2026)

## Abstract (arXiv)

Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific applications. Although Vision-Language Models (VLMs) demonstrate significant advantages in generalization capabilities, their performance in industrial anomaly detection remains limited. To address this challenge, we propose IAD-R1, a universal post-training framework applicable to VLMs of different architectures and parameter scales, which substantially enhances their anomaly detection capabilities. IAD-R1 employs a two-stage training strategy: the Perception Activation Supervised Fine-Tuning (PA-SFT) stage utilizes a meticulously constructed high-quality Chain-of-Thought dataset (Expert-AD) for training, enhancing anomaly perception capabilities and establishing reasoning-to-answer correlations; the Structured Control Group Relative Policy Optimization (SC-GRPO) stage employs carefully designed reward functions to achieve a capability leap from "Anomaly Perception" to "Anomaly Interpretation". Experimental results demonstrate that IAD-R1 achieves significant improvements across 7 VLMs, the largest improvement was on the DAGM dataset, with average accuracy 43.3% higher than the 0.5B baseline. Notably, the 0.5B parameter model trained with IAD-R1 surpasses commercial models including GPT-4.1 and Claude-Sonnet-4 in zero-shot settings, demonstrating the effectiveness and superiority of IAD-R1. The dataset, code, and all model weights will be publicly available at https://github.com/Yanhui-Lee/IAD-R1.