3 papers
cs.AI2026
ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience
Jia Luo
AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforcement learning (RL) optimize…
cs.LG2023
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation
Yuxing Tian, Mingjie Zhu, Jiachi Luo +1
This study focuses on long-term forecasting (LTF) on continuous-time dynamic graph networks (CTDGNs), which is important for real-world modeling. Existing CTDGNs are effective for…
cs.LG2022
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction
Yuxing Tian, Zheng Liu, Yanwen Qu +2
Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. I…