5 papers
LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models
Yihong Tang, Menglin Kong, Junlin He +3
Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable when individual-level mobility…
Active Simulation-Based Inference for Scalable Car-Following Model Calibration
Menglin Kong, Chengyuan Zhang, Lijun Sun
Credible microscopic traffic simulation requires car-following models that capture both the average response and the substantial variability observed across drivers and situations.…
Frequency-Constrained Learning for Long-Term Forecasting
Menglin Kong, Vincent Zhihao Zheng, Lijun Sun
Many real-world time series exhibit strong periodic structures arising from physical laws, human routines, or seasonal cycles. However, modern deep forecasting models often fail to…
Dynamic Modes as Time Representation for Spatiotemporal Forecasting
Menglin Kong, Vincent Zhihao Zheng, Xudong Wang +1
This paper introduces a data-driven time embedding method for modeling long-range seasonal dependencies in spatiotemporal forecasting tasks. The proposed approach employs Dynamic M…
A Survey on Vision-Language-Action Models for Autonomous Driving
Sicong Jiang, Zilin Huang, Kangan Qian +17
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…