# PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection

arXiv: https://arxiv.org/abs/2412.12154 · HTML: https://mlsanity.com/paper/2412.12154/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs](https://mlsanity.com/paper/2206.10071/) (2022)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [Panoptic Scene Graph Generation with Semantics-Prototype Learning](https://mlsanity.com/paper/2307.15567/) (2023)
- [AutoAudit: Mining Accounting and Time-Evolving Graphs](https://mlsanity.com/paper/2011.00447/) (2020)
- [ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels](https://mlsanity.com/paper/2208.11290/) (2022)
- [Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features](https://mlsanity.com/paper/1804.04888/) (2018)

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

- [AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://mlsanity.com/paper/2412.11142/) (2024)
- [SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning](https://mlsanity.com/paper/2504.10369/) (2025)
- [FORTIS: Benchmarking Over-Privilege in Agent Skills](https://mlsanity.com/paper/2605.09163/) (2026)
- [ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models](https://mlsanity.com/paper/2502.11059/) (2025)
- [The Autonomy Tax: Defense Training Breaks LLM Agents](https://mlsanity.com/paper/2603.19423/) (2026)
- [AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection](https://mlsanity.com/paper/2505.12594/) (2025)
- [ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes](https://mlsanity.com/paper/2603.19497/) (2026)
- [Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics](https://mlsanity.com/paper/2509.03290/) (2025)
- [Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts](https://mlsanity.com/paper/2505.04507/) (2025)
- [MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings](https://mlsanity.com/paper/2505.17972/) (2025)
- [UniOD: A Universal Model for Outlier Detection across Diverse Domains](https://mlsanity.com/paper/2507.06624/) (2025)
- [Anatomy of a failure: When, how, and why deep vision fails in scientific domains](https://mlsanity.com/paper/2605.04231/) (2026)
- [Agent Safety Is Action Alignment](https://mlsanity.com/paper/2606.28739/) (2026)
- [RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection](https://mlsanity.com/paper/2606.28970/) (2026)
- [Real-time and adaptive anomaly detection algorithm for cyclostationary models](https://mlsanity.com/paper/2609.09326/) (2026)
- [TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks](https://mlsanity.com/paper/2609.36968/) (2026)
- [Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes](https://mlsanity.com/paper/2609.38424/) (2026)

## Abstract (arXiv)

Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation. Among open-source libraries for outlier detection, the Python Outlier Detection (PyOD) library is the most widely adopted, with over 8,500 GitHub stars, 25 million downloads, and diverse industry usage. However, PyOD currently faces three limitations: (1) insufficient coverage of modern deep learning algorithms, (2) fragmented implementations across PyTorch and TensorFlow, and (3) no automated model selection, making it hard for non-experts. To address these issues, we present PyOD Version 2 (PyOD 2), which integrates 12 state-of-the-art deep learning models into a unified PyTorch framework and introduces a large language model (LLM)-based pipeline for automated OD model selection. These improvements simplify OD workflows, provide access to 45 algorithms, and deliver robust performance on various datasets. In this paper, we demonstrate how PyOD 2 streamlines the deployment and automation of OD models and sets a new standard in both research and industry. PyOD 2 is accessible at [https://github.com/yzhao062/pyod](https://github.com/yzhao062/pyod). This study aligns with the Web Mining and Content Analysis track, addressing topics such as the robustness of Web mining methods and the quality of algorithmically-generated Web data.