arXiv AI

Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles

arXiv:2607. 08373v1 Announce Type: cross Abstract: Connected vehicles are autonomous cyber-physical systems whose behavior must be continuously monitored during operation to detect deviations from normal operation before they propagate into failures.

Hugging Face Trending Papers

Jul 14

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training.

agents reinforcement-learning benchmarks

arXiv AI

Jul 15

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

arXiv:2607. 12523v1 Announce Type: cross Abstract: Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts.

By Emil Mittag, Richard Dazeley, Peter Vamplew

agents reinforcement-learning 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 AI

Aug 11

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.

By Jiyong Kwon, Ujin Jeon, Sooji Lee, Guang Lin

llms agents

arXiv Machine Learning

Aug 4

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.

By Xudong Mou, Rui Wang, Tiejun Wang, Zexin Wu, Fangda Guo, Jie Sun, Shiru Chen, Penghao Zhang, Tiezi Zhang, Tianyu Wo, Hao Peng, Chunming Hu, Xudong Liu, Renyu Yang

benchmarks

arXiv Machine Learning

Jun 11

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

arXiv:2606. 11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance.

By Dayananda Herurkar, Federico Raue, Joachim Folz, J\"orn Hees, Andreas Dengel

fine-tuning efficiency

arXiv Machine Learning

Jun 10

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

arXiv:2603. 10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational technology and networked environments.

By Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen, Kimmo K. Kaski

arXiv AI

Aug 11

VTO: Visual Tool Orchestration for Video Anomaly Detection

arXiv:2608. 08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios.

By Rui Wang, Yeteng Wu, Xianling Zhang, Mengshi Qi

agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

6d ago

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

arXiv:2608. 12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response.

By Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan, S M Ahsan Habib, Sonjoy Kumar Dey, Md. Salahuddin Gazi, Molay Kumar Roy, Asaduzzaman Anik

agents reinforcement-learning

arXiv AI

6d ago

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.

By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang

llms agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection

arXiv:2606. 08153v1 Announce Type: cross Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure.

By David Eje, Tanmay Sharma, Khush Patel, Manuel Mazzara, Leonard Johard

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression

arXiv:2512. 13956v4 Announce Type: replace-cross Abstract: Cloud-native systems have made operational work both more powerful and harder to automate: incidents unfold across microservices, logs and metrics arrive faster than operators can inspect them, and recovery actions must be coordinated without losing the causal context that makes them safe.

By Zishan Bai, Hanxuan Chen, Jiayi Gu, Wenqian Weng, Enze Ge, Jiacheng Shi, Yichao Zhang, Zhimo Han, Riyang Bao, Xinyuan Song, Jacqueline Pang, Junfeng Hao

llms agents

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