4 papers · 1 filter
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Shuhao Li, Weidong Yang, Yue Cui +4
Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations…
LogReasoner: Empowering LLMs with Expert-like Coarse-to-Fine Reasoning for Automated Log Analysis
Lipeng Ma, Yixuan Li, Weidong Yang +7
Log analysis is crucial for monitoring system health and diagnosing failures in complex systems. Recent advances in large language models (LLMs) offer new opportunities for automat…
Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation
Shuhao Li, Weidong Yang, Yue Cui +4
Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining…
ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation
Xinyi Liu, Lipeng Ma, Yixuan Li +5
Large Language Models (LLMs) are widely used across various scenarios due to their exceptional reasoning capabilities and natural language understanding. While LLMs demonstrate str…