5 papers
LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection
Dezheng Wang, Tong Chen, Guansong Pang +3
As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of…
Beyond Normal References: Discriminative Few-Shot Anomaly Detection
Huan Wang, Jun Shen, Jun Yan +1
This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available a…
VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems
Hezhe Qiao, Hanghang Tong, Ee-Peng Lim +2
Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agents remain a key bottleneck to system-level reliability. Automatic failure attri…
Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
Wei Huang, Yuxuan Xiong, Hezhe Qiao +3
Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, ex…
Evolutionary Router Feature Generation for Zero-Shot Graph Anomaly Detection with Mixture-of-Experts
Haiyang Jiang, Tong Chen, Xinyi Gao +3
Zero-shot graph anomaly detection (GAD) has attracted increasing attention recent years, yet the heterogeneity of graph structures, features, and anomaly patterns across graphs mak…