arXiv AI July 14, 2026 By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler

ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation

arXiv:2603. 10926v2 Announce Type: replace-cross Abstract: Automotive anomaly detectors are often selected from accuracy only benchmarks on workstation class hardware, whereas in-vehicle monitoring requires predictable scoring latency under limited CPU parallelism.

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 arXiv AI.

arXiv AI

Aug 5

STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection

arXiv:2511. 15339v3 Announce Type: replace-cross Abstract: Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging.

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

benchmarks

arXiv Machine Learning

Jul 1

Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions

arXiv:2605. 24251v2 Announce Type: replace Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints.

By Chad Weatherly, Sen Lin

efficiency benchmarks

arXiv AI

2d ago

Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

arXiv:2610.01223v1 Announce Type: cross Abstract: Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as th...

By David Berghaus

llms agents benchmarks safety

arXiv AI

Sep 15

Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection

arXiv:2603.12916v4 Announce Type: replace-cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonom...

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

agents robotics

arXiv Machine Learning

Jun 10

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.

By Guillaume Coulaud (UM, IROKO), Reza Akbarinia (IROKO), Florent Masseglia (IROKO)

llms benchmarks

arXiv Machine Learning

Sep 15

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...

By Abigail Langbridge, Fearghal O'Donncha, James T Rayfield, Bradley Eck

benchmarks

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STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection

Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions

Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data