2 citations · 7 across the 6 of their papers we have counts for
6 papers
Memory in the Age of AI Agents
Yuyang Hu, Shichun Liu, Yanwei Yue +44
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…
Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects
Yixin Liu, Guibin Zhang, Kun Wang +2
Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in a wide range of applications, including web navigation, software development, a…
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Yifan Hu, Guibin Zhang, Peiyuan Liu +6
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD) strategies. While CI overlooks important covariate rela…
LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models
Miao Yu, Junfeng Fang, Yingjie Zhou +4
While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems,…
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Kun Wang, Guibin Zhang, Xinnan Zhang +6
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily,…
Graph Sparsification via Mixture of Graphs
Guibin Zhang, Xiangguo Sun, Yanwei Yue +4
Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale gr…