arXiv AI

Grounded Well-Condition Anomaly Detection on the Volve Field: Constructed Labels, a Baseline, and a Dual-Head Model

arXiv:2608. 05685v1 Announce Type: new Abstract: Most public benchmarks for machine-condition monitoring come from test rigs, where faults are induced on purpose and every event is known.

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 Machine Learning

5d ago

Excess Separability: Nuisance-Controlled Residual-Stream Probing for Benchmark Contamination Detection

arXiv:2608. 12652v1 Announce Type: cross Abstract: Benchmark contamination is diagnosed today with n-gram overlap, with likelihood-based membership inference, or with canary strings, and each needs something usually unavailable: the training corpus, a well-chosen test statistic, or foresight at dataset release.

By Florian Braun

llms benchmarks

arXiv AI

Jun 2

Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark

arXiv:2605. 26068v3 Announce Type: replace-cross Abstract: Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision.

By Xu Yao, Siyuan Zhou, Zhenbo Wu, Chaochuan Hou, Shuang Liang, Shiping Wang, Hailiang Huang, Songqiao Han, Minqi Jiang

benchmarks

arXiv AI

Aug 6

Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection

arXiv:2604. 17388v3 Announce Type: replace-cross Abstract: Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables.

By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler

benchmarks safety

arXiv Machine Learning

Jul 21

FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection

arXiv:2605. 22779v2 Announce Type: replace-cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible.

By Huanchi Wang, Zihang Huang, Yifang Tian, Kristina Dzeparoska, Hans-Arno Jacobsen, Alberto Leon-Garcia

llms

arXiv Machine Learning

Aug 6

An Explainable LLM Agent Layer for Open-World Anomaly Detection in Oil Wells

arXiv:2608. 04041v1 Announce Type: new Abstract: Open-World Learning (OWL) pipelines for oil well anomaly detection have recently been shown to combine autoencoder-based detection, multiclass classification, and Mahalanobis-based novelty detection on the public 3W dataset.

By Lucas Gouveia Omena Lopes, Thales Miranda de Almeida Vieira, Eduardo Toledo de Lima Junior, William Wagner Matos Lira

llms agents

arXiv Machine Learning

Jul 30

Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

arXiv:2607. 26704v1 Announce Type: cross Abstract: Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies.

By L\'ea Billet (LAAS, INSA Toulouse, ANITI), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS), Alexandre Gaffet

arXiv AI

Jun 3

The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection

arXiv:2606. 03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment.

By Wojciech Zarzecki, Jan Dubi\'nski, Sebastian Cygert

llms benchmarks

arXiv Machine Learning

Jun 30

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.

By Isao Kurosawa

llms 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

arXiv Machine Learning

Jul 7

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.

By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva

safety

arXiv Machine Learning

2d ago

Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.

By Haoyi Jia, Sagar Addepalli, Julia Gonski

rag

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