6 papers
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…
VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines
Kai Huang, Houdong Liang, Chongchong Yao +5
Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines…
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…
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
Shuhao Li, Yue Cui, Jingyi Xu +5
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advan…
Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists
Yue Cui, Liuyi Yao, Shuchang Tao +4
Large language models (LLMs) have significantly advanced natural language processing, particularly through the integration of external tools and APIs. However, their effectiveness…
Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspection
Yue Cui, Liuyi Yao, Zitao Li +3
Multi-agent systems based on large language models (LLMs) advance automatic task completion in various fields, where debate is a common cooperation form for agents to solve complic…