7 papers
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Rui Miao +5
Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly autonomous in various safety-critical…
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
Junjun Pan, Yixin Liu, Chuan Zhou +3
Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume ide…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Bo Li, Yingqi Feng, Ming Jin +10
Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. T…
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng +4
Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic conn…
Graph Retrieval-Augmented LLM for Conversational Recommendation Systems
Zhangchi Qiu, Linhao Luo, Zicheng Zhao +2
Conversational Recommender Systems (CRSs) have emerged as a transformative paradigm for offering personalized recommendations through natural language dialogue. However, they face…