4 citations · 4 across the 8 of their papers we have counts for
8 papers
MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents
Jifeng Gao, Kang Xia, Yi Zhang +5
Persistent external memory enhances agent continuity but introduces persistent security vulnerabilities: adversarial content can be injected via standard interaction channels, reta…
Multimodal Graph Representation Learning with Dynamic Information Pathways
Xiaobin Hong, Mingkai Lin, Xiaoli Wang +2
Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requi…
Energy-Aware Pattern Disentanglement: A Generalizable Pattern Assisted Architecture for Multi-task Time Series Analysis
Xiangkai Ma, Xiaobin Hong, Wenzhong Li +1
Time series analysis has found widespread applications in areas such as weather forecasting, anomaly detection, and healthcare. While deep learning approaches have achieved signifi…
Semantic-Supervised Spatial-Temporal Fusion for LiDAR-based 3D Object Detection
Chaoqun Wang, Xiaobin Hong, Wenzhong Li +1
LiDAR-based 3D object detection presents significant challenges due to the inherent sparsity of LiDAR points. A common solution involves long-term temporal LiDAR data to densify th…
Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting
Xiaobin Hong, Jiawen Zhang, Wenzhong Li +2
The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existin…
Domain Fusion Controllable Generalization for Cross-Domain Time Series Forecasting from Multi-Domain Integrated Distribution
Xiangkai Ma, Xiaobin Hong, Mingkai Lin +3
Conventional deep models have achieved unprecedented success in time series forecasting. However, facing the challenge of cross-domain generalization, existing studies utilize stat…
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series
Xiangkai Ma, Xiaobin Hong, Wenzhong Li +1
Time series analysis is a fundamental data mining task that supervised training methods based on empirical risk minimization have proven their effectiveness on specific tasks and d…
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Qifan Zhang, Xiaobin Hong, Jianheng Tang +5
Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm fo…