arXiv Machine Learning June 5, 2026 By Mulugeta Weldezgina Asres, Christian Walter Omlin, The CMS-HCAL Collaboration

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data

Read the original on arXiv Machine Learning →

arXiv:2412. 11800v4 Announce Type: replace Abstract: Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults.

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 Machine Learning.

efficiency

arXiv Machine Learning

Jul 21

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

arXiv:2607. 18127v1 Announce Type: cross Abstract: With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability.

By Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy, Ngoc-Thanh Nguyen, Rogardt Heldal, Patrizio Pelliccione

efficiency benchmarks

arXiv Machine Learning

Jun 10

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.

By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao

agents safety

arXiv Machine Learning

Aug 21

LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

arXiv:2608. 19463v1 Announce Type: new Abstract: Anomaly detection in tabular data is challenging because abnormal samples often arise as violations of cross-feature dependencies rather than simple marginal deviations.

By Tu Anh Hoang Nguyen, Dang Nguyen, Thuc Duy Le, Trung Le, Sunil Gupta

llms fine-tuning benchmarks

arXiv AI

Aug 28

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.

By Hugo Math

llms agents safety

arXiv Machine Learning

Jul 31

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.

By Lei Zan, Keli Zhang, Shifeng Xie, Jiale Zheng, Zehao Xiao, Zhiwei Dong, Ke Zhang, Ruichu Cai, Malik Tiomoko, Lujia Pan

llms benchmarks safety

arXiv Machine Learning

Jun 17

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604. 17616v3 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems.

By Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker, Jason Rambach

rag benchmarks

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ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

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EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

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