# LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions

arXiv: https://arxiv.org/abs/2503.20252 · HTML: https://mlsanity.com/paper/2503.20252/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://mlsanity.com/paper/2210.07829/) (2022)
- [Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations](https://mlsanity.com/paper/2410.02762/) (2024)
- [Why are Visually-Grounded Language Models Bad at Image Classification?](https://mlsanity.com/paper/2405.18415/) (2024)
- [Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem](https://mlsanity.com/paper/2411.00238/) (2024)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (2021)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction](https://mlsanity.com/paper/2501.01767/) (2025)
- [Component-aware anomaly detection framework for adjustable and logical industrial visual inspection](https://mlsanity.com/paper/2305.08509/) (2023)
- [Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection](https://mlsanity.com/paper/2312.13783/) (2023)
- [LogiCode: an LLM-Driven Framework for Logical Anomaly Detection](https://mlsanity.com/paper/2406.04687/) (2024)
- [CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection](https://mlsanity.com/paper/2408.15628/) (2024)
- [GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality](https://mlsanity.com/paper/2501.13493/) (2025)
- [UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection](https://mlsanity.com/paper/2307.12540/) (2023)
- [Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types](https://mlsanity.com/paper/2409.09269/) (2024)
- [Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization](https://mlsanity.com/paper/2406.19502/) (2024)
- [A general-purpose method for applying Explainable AI for Anomaly Detection](https://mlsanity.com/paper/2207.11564/) (2022)
- [ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling](https://mlsanity.com/paper/2404.16659/) (2024)
- [Decompose and Compare Consistency: Measuring VLMs' Answer Reliability via Task-Decomposition Consistency Comparison](https://mlsanity.com/paper/2407.07840/) (2024)
- [Interactive Explainable Anomaly Detection for Industrial Settings](https://mlsanity.com/paper/2410.12817/) (2024)

## Built on by (most cited first, 9 of 9)

- [IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection](https://mlsanity.com/paper/2508.09178/) (2025)
- [Hold Onto That Thought: Assessing KV Cache Compression On Reasoning](https://mlsanity.com/paper/2512.12008/) (2025)
- [FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios](https://mlsanity.com/paper/2604.07413/) (2026)
- [Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection](https://mlsanity.com/paper/2609.17211/) (2026)
- [TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank](https://mlsanity.com/paper/2508.21795/) (2025)
- [DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models](https://mlsanity.com/paper/2606.16601/) (2026)
- [LADBench: A Benchmark for Logical Fault Detection in Images](https://mlsanity.com/paper/2606.17433/) (2026)
- [Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models](https://mlsanity.com/paper/2607.14049/) (2026)
- [Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities](https://mlsanity.com/paper/2608.12374/) (2026)

## Abstract (arXiv)

Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it a vital tool in process control. Logical anomalies may appear visually normal yet violate predefined constraints on object presence, arrangement, or quantity, depending on reasoning and explainability. We introduce LogicQA, a framework that enhances AD by providing industrial operators with explanations for logical anomalies. LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints. LogicQA is training-free, annotation-free, and operates in a few-shot setting. We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6 percent and an F1-max of 87.0 percent along with the explanations of anomalies. Also, our approach has shown outstanding performance on semiconductor SEM corporate data, further validating its effectiveness in industrial applications.