Hugging Face Trending Papers June 20, 2026

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection

Read the original on Hugging Face Trending Papers →

Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event has departed from normal behavior. We study a normal-world modeling formulation for this setting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

benchmarks

arXiv Machine Learning

Sep 1

Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

arXiv:2608.30323v1 Announce Type: new Abstract: Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require s...

By Stefan Jonas, Angela Meyer

fine-tuning safety

arXiv AI

Jun 2

Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie

arXiv Machine Learning

Jun 9

Normality Calibration in Semi-supervised Graph Anomaly Detection

arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.

By Guolei Zeng, Hezhe Qiao, Guoguo Ai, Jinsong Guo, Guansong Pang

safety

arXiv Machine Learning

Jun 17

When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts

arXiv:2606. 17572v1 Announce Type: new Abstract: Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector.

By Yifan Wang

arXiv AI

Aug 5

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

arXiv:2608. 03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment.

By Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang

diffusion safety

arXiv Machine Learning

2d ago

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

arXiv:2609.38789v1 Announce Type: new Abstract: Reconstruction errors in multivariate time-series anomaly detection may not reliably distinguish abnormal behavior from benign deviations. Language-der...

By Jahyeob Koo, Kio Yun, Byoungmo Koo, Jun-Geol Baek

llms multimodal benchmarks safety

One email a morning, machine-written

Related stories

Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

Normality Calibration in Semi-supervised Graph Anomaly Detection

When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection