105 citations · 478 across the 66 of their papers we have counts for
27 papers · 1 filter
PID-controlled Langevin Dynamics for Faster Sampling of Generative Models
Hongyi Chen, Jianhai Shu, Jingtao Ding +2
Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introdu…
Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructions
Zhaoyi Li, Jingtao Ding, Yong Li +1
Flow Matching (FM) models achieve remarkable results in generative tasks. Building upon diffusion models, FM's simulation-free training paradigm enables simplicity and efficiency b…
Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting
Wanjin Feng, Yuan Yuan, Jingtao Ding +1
In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark leaderboards. However, this approach suff…
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…
ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness
Chang Liu, Bohao Zhao, Jingtao Ding +1
Foundation models have shown great promise in achieving zero-shot or few-shot forecasting for ODE-based chaotic systems via large-scale pretraining. However, existing architectures…
Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
Zihan Yu, Guanren Wang, Jingtao Ding +2
Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is…
UniMove: A Unified Model for Multi-city Human Mobility Prediction
Chonghua Han, Yuan Yuan, Yukun Liu +3
Human mobility prediction is vital for urban planning, transportation optimization, and personalized services. However, the inherent randomness, non-uniform time intervals, and com…
Advancing network resilience theories with symbolized reinforcement learning
Yu Zheng, Jingtao Ding, Depeng Jin +2
Many complex networks display remarkable resilience under external perturbations, internal failures and environmental changes, yet they can swiftly deteriorate into dysfunction upo…
FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agents
Qinglong Yang, Haoming Li, Haotian Zhao +4
Mobile GUI agents are becoming critical tools to improve user experience on smart devices, with multimodal large language models (MLLMs) emerging as the dominant paradigms in this…
A Survey of Physics-Informed AI for Complex Urban Systems
En Xu, Huandong Wang, Yunke Zhang +8
Urban systems are typical examples of complex systems, where the integration of physics-based modeling with artificial intelligence (AI) presents a promising paradigm for enhancing…
OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data
Jinwei Zeng, Yu Liu, Guozhen Zhang +4
Accurately estimating high-resolution carbon emissions is crucial for effective emission governance and mitigation planning. While conventional methods for precise carbon accountin…
Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems
Jiazhen Liu, Ruikun Li, Huandong Wang +4
This position paper argues that next-generation non-equilibrium-inspired generative models will provide the essential foundation for better modeling real-world complex dynamical sy…
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…
Large language model as user daily behavior data generator: balancing population diversity and individual personality
Haoxin Li, Jingtao Ding, Jiahui Gong +1
Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by…
A Global Commuting Origin-Destination Flow Dataset for Urban Sustainable Development
Can Rong, Jingtao Ding, Meng Li +1
Commuting Origin-Destination (OD) flows capture movements of people from residences to workplaces, representing the predominant form of intra-city mobility and serving as a critica…
Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities
Can Rong, Xin Zhang, Yanxin Xi +3
Commuting Origin-destination~(OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is chall…
PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models
Chang Liu, Bohao Zhao, Jingtao Ding +2
Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and i…
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…
Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
Ruikun Li, Huandong Wang, Jingtao Ding +3
Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce ap…
A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science
Jie Feng, Jinwei Zeng, Qingyue Long +15
Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However,…
WorldMove, a global open data for human mobility
Yuan Yuan, Yuheng Zhang, Jingtao Ding +1
High-quality human mobility data is crucial for applications such as urban planning, transportation management, and public health, yet its collection is often hindered by privacy c…
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
Hongyi Chen, Jingtao Ding, Xiaojun Liang +2
Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. E…
Sample-Efficient Diffusion-based Control of Complex Physics Systems
Hongyi Chen, Jingtao Ding, Jianhai Shu +4
Controlling complex physics systems is important in diverse domains. While diffusion-based methods have demonstrated advantages over classical model-based approaches and myopic seq…