mlsanityData to 14 Oct 2026

LogiCode: an LLM-Driven Framework for Logical Anomaly Detection

arXiv 2406.04687 ↗First posted 7 Jun 2024cs.LG cs.CVcited by 19

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Abstract (arXiv)

This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies. By harnessing LLMs for logical reasoning, LogiCode autonomously generates Python codes to pinpoint anomalies such as incorrect component quantities or missing elements, marking a significant leap forward in anomaly detection technologies. A custom dataset "LOCO-Annotations" and a benchmark "LogiBench" are introduced to evaluate the LogiCode's performance across various metrics including binary classification accuracy, code generation success rate, and precision in reasoning. Findings demonstrate LogiCode's enhanced interpretability, significantly improving the accuracy of logical anomaly detection and offering detailed explanations for identified anomalies. This represents a notable shift towards more intelligent, LLM-driven approaches in industrial anomaly detection, promising substantial impacts on industry-specific applications.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

28 of 41 references.

References · 28

  1. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  2. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,457
  3. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  5. 2023LISA: Reasoning Segmentation via Large Language Modelcited by 776
  6. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  7. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 603
  8. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  9. 2022Visual Programming: Compositional visual reasoning without trainingcited by 516
  10. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  11. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  12. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
16 more
  1. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  2. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 297
  3. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  4. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  5. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 171
  6. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  7. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  8. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 57
  9. 2023Prompt Engineering a Prompt Engineercited by 53
  10. 2023IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Modelscited by 47
  11. 2023OceanGPT: A Large Language Model for Ocean Science Taskscited by 43
  12. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  13. 2023GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detectioncited by 23
  14. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  15. 2022A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturingcited by 16
  16. 2023SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classificationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 13; 2026: 4.

19 papers.

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  1. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 47
  2. 2025Visual Large Language Models for Generalized and Specialized Applicationscited by 36
  3. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  4. 2025Towards Training-free Anomaly Detection with Vision and Language Foundation Modelscited by 26
  5. 2025LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extractioncited by 25
  6. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 17
  7. 2025Chameleon: Fast-slow Neuro-symbolic Lane Topology Extractioncited by 16
  8. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 10
  9. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 9
  10. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  11. 2025Agentic AI for autonomous anomaly management in complex systemscited by 4
  12. 2025Foundation Models for Anomaly Detection: Vision and Challengescited by 3
7 more
  1. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  2. 2025ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantificationcited by 2
  3. 2026EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Modelscited by 2
  4. 2025Zero-Shot Multi-Criteria Visual Quality Inspection for Semi-Controlled Industrial Environments via Real-Time 3D Digital Twin Simulationcited by 0
  5. 2026PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarioscited by 0
  6. 2026From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Modelscited by 0
  7. 2026Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomaliescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
  2. 2025LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection
  3. 2026AnomalyGen: Enhancing Log-Based Anomaly Detection with Code-Guided Data Augmentation ↗
  4. 2025SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment
  5. 2026Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
  6. 2026Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning
  7. 2025Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models
  8. 2025RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration