9 papers
CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection
Yunfeng Zhao, Qingfeng Chen, Yue Tan +4
The paper introduces CORE, a unified approach for detecting anomalies in tabular data that aligns heterogeneous features into a common space and uses in-context reconstruction of n…
DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
Xin Zhang, Yili Wang, Yue Tan +6
Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs remain vulnerable to hallucinatio…
TRE: Training-Free Hallucination Detection for Diffusion Language Models
Pengcheng Weng, Yanyu Qian, Yue Tan +1
Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem. Existing hallucina…
Towards Anomaly Detection on Relational Data
Shiyuan Li, Yunfeng Zhao, Yue Tan +3
Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and…
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3
Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering…
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks
Rui Miao, Yixin Liu, Yili Wang +5
The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through int…