# Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision

arXiv: https://arxiv.org/abs/2603.27179 · HTML: https://mlsanity.com/paper/2603.27179/

## Key facts

- Cited by 3 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 18 of its 56 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 18 of 18)

- [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)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [A Survey on Benchmarks of Multimodal Large Language Models](https://mlsanity.com/paper/2408.08632/) (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)
- [VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON](https://mlsanity.com/paper/2306.07890/) (2023)
- [OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning](https://mlsanity.com/paper/2505.22039/) (2025)
- [Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset](https://mlsanity.com/paper/2311.04095/) (2023)
- [ONER: Online Experience Replay for Incremental Anomaly Detection](https://mlsanity.com/paper/2412.03907/) (2024)

## Built on by (most cited first, 3 of 3)

- [OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation](https://mlsanity.com/paper/2607.18850/) (2026)
- [VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2607.29370/) (2026)
- [OSAGEN: Object-Aware Mask Priors and Multistage Decoupled Diffusion for Industrial Anomaly Generation](https://mlsanity.com/paper/2607.29533/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning and perceptual abilities for anomaly detection. However, most approaches remain confined to image-level anomaly detection and textual reasoning, while pixel-level localization still relies on external vision modules and dense annotations. In this work, we activate the intrinsic reasoning potential of MLLMs to perform anomaly detection, pixel-level localization, and interpretable reasoning solely from image-level supervision, without any auxiliary components or pixel-wise labels. Specifically, we propose Reasoning-Driven Anomaly Localization (ReAL), which extracts anomaly-related tokens from the autoregressive reasoning process and aggregates their attention responses to produce pixel-level anomaly maps. We further introduce a Consistency-Guided Reasoning Optimization (CGRO) module that leverages reinforcement learning to align reasoning tokens with visual attentions, resulting in more coherent reasoning and accurate anomaly localization. Extensive experiments on four public benchmarks demonstrate that our method significantly improves anomaly detection, localization, and interpretability. Remarkably, despite relying solely on image-level supervision, our approach achieves performance competitive with MLLM-based methods trained under dense pixel-level supervision. Code is available at https://github.com/YizhouJin313/ReADL.