From the 3 of 14 linked papers with an AI index.
14 papers
Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
Liangjun You, Min Wu, Orlando Woods +1
Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations. Current forecastin…
Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
Xu Zheng, Wei Cheng, Zhuomin Chen +3
The paper presents IB-Forecast, an interpretable multivariate time-series forecasting model that uses an information bottleneck to generate sparse, faithful explanations of predict…
Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
Xu Zheng, Zhuomin Chen, Chaohao Lin +4
The paper introduces Trajectory Graph Copilot, a framework that builds probabilistic graphs of past agent trajectories and uses a graph neural network to flag potentially erroneous…
Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs
Xu Zheng, Chaohao Lin, Zhuomin Chen +4
The paper introduces GAMER, a graph‑based action‑centric memory system that stores past reasoning as a dynamic graph and uses temporal‑difference learning to estimate action values…
Uncovering Insights of Compound Flooding with Data-Driven AI
Xu Zheng, Chaohao Lin, Sipeng Chen +7
Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approache…
LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation
Shouwei Gao, Xu Zheng, Dongsheng Luo +2
The rapid growth of scientific machine learning (SciML) has accelerated discovery across diverse domains, yet designing effective SciML models remains a challenging task. In practi…