arXiv Machine Learning

ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality

arXiv:2603. 25538v3 Announce Type: replace Abstract: Automated incident management is critical for microservice reliability.

arXiv Machine Learning

Sep 16

Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction

The paper introduces a multimodal anomaly detection framework that uses cross‑modal reconstruction of heterogeneous time‑series sensor data to detect faults in industrial systems. By learning to reconstruct each modality from the others, the method leverages complementary information across sensing channels without requiring explicit temporal alignment or identical sampling rates. An adaptive test‑time thresholding mechanism further improves robustness to distribution shifts caused by changing operating conditions, as demonstrated by strong fault detection performance in three industrial case studies, especially under out‑of‑distribution regimes.

By Magnus Munk Jensen, Dorte Hammersh{\o}i, Rafa{\l} Wi\'sniewski, Olga Fink

multimodal safety

arXiv AI

Jul 17

VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.

By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das

llms multimodal benchmarks

arXiv Machine Learning

2d ago

MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion

arXiv:2605.15235v2 Announce Type: replace Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two...

By Wugeng Zheng, Ziwen Kan, Tianyu Hu, Tianlong Chen, Chen Chen, Song Wang

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 4

Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC

arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.

By Haolu Liu, Xiyue Wang, Xuanting Xie, Liangjian Wen, Zhao Kang

llms benchmarks

arXiv AI

Aug 11

GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices

arXiv:2608. 08968v1 Announce Type: cross Abstract: Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs.

By Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Yiran Li, Hans-Arno Jacobsen

llms agents multimodal benchmarks safety

arXiv Machine Learning

Aug 6

MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements

arXiv:2506. 02260v4 Announce Type: replace-cross Abstract: Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data.

By Howon Ryu, Yuliang Chen, Yacun Wang, Andrea Z. LaCroix, Chongzhi Di, Loki Natarajan, Yu Wang, Jingjing Zou

llms multimodal benchmarks

arXiv Computer Vision

Oct 15

CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis

arXiv:2610.19610v1 Announce Type: new Abstract: Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving do...

By Haoxiang Rao, Fang Zhao, Zhao Wang, Weijia Li, Chenyang Si, Caifeng Shan

multimodal

arXiv AI

Aug 10

FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition

arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).

By Ghani Haider, Majid Kundroo, Boyun Eom, Dong Hwan Park, Chen Chen, Taehong Kim

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 16

Unsupervised Learning for Missing Modalities in Multimodal Learning

arXiv:2606. 15743v1 Announce Type: new Abstract: This paper addresses the missing-modality challenge in multi-modal learning by introducing Unsupervised Learning for Missing Modalities in Multi-Modal Learning (UL4M4), a flexible framework that imputes missing feature embeddings in a task-independent manner before supervised prediction.

By Hassan Ismkhan, Hamid Bouchahcia

rag multimodal benchmarks

arXiv Machine Learning

Jul 21

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning

arXiv:2607. 16681v1 Announce Type: new Abstract: Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance.

By Umair bin Mansoor, Munaf Rashid, Roomi Naqvi

rag fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 7

MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health

arXiv:2506. 02260v5 Announce Type: replace-cross Abstract: Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data.

By Howon Ryu, Yuliang Chen, Yacun Wang, Andrea Z. LaCroix, Chongzhi Di, Loki Natarajan, Yu Wang, Jingjing Zou

llms multimodal benchmarks

arXiv Machine Learning

Jun 16

SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

arXiv:2606. 15559v1 Announce Type: cross Abstract: The transition toward software-defined vehicles concentrates an increasing share of vehicle functionality into distributed software services, where failures propagate through service dependencies and the surface symptom is often several causal hops away from the underlying defect.

By Matthias Wei{\ss}, Athreya Hosahalli Prakash, Falk Dettinger, Nasser Jazdi, Michael Weyrich

rag agents multimodal

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