13 papers
Normality Calibration in Semi-supervised Graph Anomaly Detection
Guolei Zeng, Hezhe Qiao, Guoguo Ai +2
Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subse…
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…
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
Wei Huang, Hezhe Qiao, Kailai Zhang +3
Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples that deviate from these patter…
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…
Identifying Good and Bad Neurons for Task-Level Controllable LLMs
Wenjie Li, Guansong Pang, Hezhe Qiao +2
Large Language Models have demonstrated remarkable capabilities on multiple-choice question answering benchmarks, but the complex mechanisms underlying their large-scale neurons re…
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
Hui He, Hezhe Qiao, Yutong Chen +2
Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstrea…