activity
20242026
most citedTowards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

18 citations · 18 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation

Fanjin Meng, Jingtao Ding, Nian Li +2

Human daily behavior unfolds as complex sequences shaped by intentions, preferences, and context. Effectively modeling these behaviors is crucial for intelligent systems such as pe…

cs.LG2025

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…

cs.IR2025

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…

cs.AI2025

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…

cs.CL2025

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…

cs.AI2025★ 18 cited

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…

cs.LG2024

A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction

Jiahui Gong, Jingtao Ding, Fanjin Meng +5

Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones c…