18 citations · 18 across the 7 of their papers we have counts for
5 papers · 1 filter
MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Fanjin Meng, Yuan Yuan, Jingtao Ding +3
Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale and semantic understanding. While…
BehaveGPT: A Foundation Model for Large-scale User Behavior Modeling
Jiahui Gong, Jingtao Ding, Fanjin Meng +5
In recent years, foundational models have revolutionized the fields of language and vision, demonstrating remarkable abilities in understanding and generating complex data; however…
MoveGPT: Scaling Mobility Foundation Models with Spatially-Aware Mixture of Experts
Chonghua Han, Yuan Yuan, Jingtao Ding +3
The success of foundation models in language has inspired a new wave of general-purpose models for human mobility. However, existing approaches struggle to scale effectively due to…
Tuning Language Models for Robust Prediction of Diverse User Behaviors
Fanjin Meng, Jingtao Ding, Jiahui Gong +5
Predicting user behavior is essential for intelligent assistant services, yet deep learning models often struggle to capture long-tailed behaviors. Large language models (LLMs), wi…
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Fengli Xu, Qianyue Hao, Zefang Zong +17
Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging…