4 citations · 13 across the 8 of their papers we have counts for
8 papers
A Survey on Trustworthy LLM Agents: Threats and Countermeasures
Miao Yu, Fanci Meng, Xinyun Zhou +9
With the rapid evolution of Large Language Models (LLMs), LLM-based agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems. This evolut…
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems
Shilong Wang, Guibin Zhang, Miao Yu +5
Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, ranging from collaborative problem-solving to autonom…
NetSafe: Exploring the Topological Safety of Multi-agent Networks
Miao Yu, Shilong Wang, Guibin Zhang +6
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry. However, how to prevent…
Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model
Yifan Duan, Jian Zhao, pengcheng +8
Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data cou…
All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
Shilong Wang, Hao Wu, Yifan Duan +6
The ever-designed Graph Neural Networks, though opening a promising path for the modeling of the graph-structure data, unfortunately introduce two daunting obstacles to their deplo…
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Yifan Duan, Guibin Zhang, Shilong Wang +6
Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a…
Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Hao Wu, Yuxuan Liang, Wei Xiong +4
Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community. Many recent studies have concentrated…
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Kun Wang, Guohao Li, Shilong Wang +6
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoot…