arXiv Machine Learning

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

arXiv:2607. 06094v1 Announce Type: new Abstract: Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model normal behaviour; the standard point-adjusted evaluation, however, rewards detectors that never do.

Hugging Face Trending Papers

Jul 7

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model normal behaviour; the standard point-adjusted evaluation, however, rewards detectors that never do. CPS normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes rather than a single blob; we state this structure as ten assumptions (A1-A10), abbreviated Massive, Implicit, Imbalanced Multimodality (MIIM).

multimodal

arXiv Machine Learning

Jul 21

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607. 16811v1 Announce Type: new Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams.

By Behnam Asadi

benchmarks

arXiv Machine Learning

5d ago

Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams

arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Aug 4

Dimension-Calibrated Unexplained Mass: An Interpretable GMM Drift Statistic that Matches Kernel Two-Sample Tests

arXiv:2607. 16811v3 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Jun 15

Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches

arXiv:2606. 13780v1 Announce Type: cross Abstract: Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it.

By Jack Y. Araz, Michael Spannowsky

arXiv AI

1d ago

Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

arXiv:2608. 14666v1 Announce Type: new Abstract: Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions.

By Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal

rag multimodal safety

arXiv Machine Learning

Aug 6

Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

arXiv:2608. 05025v1 Announce Type: new Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection.

By Dmytro Knopov

arXiv Machine Learning

Jun 11

Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers

arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.

By Jun Wen Leong

llms rag safety

arXiv Machine Learning

Jul 7

Stable Global Weighting of Flow Mixtures using Simplex Exponential Moving Average

arXiv:2607. 03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries.

By Benjamin Wiriyapong, Oktay Karakus, Can Eyupoglu, Kirill Sidorov

multimodal benchmarks

arXiv AI

Jul 21

Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift

arXiv:2607. 17384v1 Announce Type: new Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles.

By Junade Ali

llms agents benchmarks

Hugging Face Trending Papers

Jun 20

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

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.

benchmarks

arXiv Machine Learning

Jul 22

RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment

arXiv:2602. 01515v2 Announce Type: replace-cross Abstract: Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, causing silent failures and potential hardware damage.

By Humphrey Munn, Brendan Tidd, Peter Bohm, Marcus Gallagher, David Howard

llms robotics

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