105 citations · 137 across the 39 of their papers we have counts for
43 papers
Inferring Hidden Structural Changes from Node-State Responses under Partial Network Observations
En Xu, Jingtao Ding, Yong Li
Online platforms often expose activity signals even when records of the underlying connections are incomplete or delayed. These activity responses may retain indirect evidence of c…
MechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?
Zihan Yu, Jiadong Zhang, Jialin Cheng +2
Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that generate them. Existing benchmarks f…
SRHarness: A Harness for Agentic Symbolic Regression
Zihan Yu, Shixuan Zhou, Hao Huang +2
Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hypotheses over long search traje…
BER-PEF: Unified Human Mobility Predictability Evaluation via Bayes Error Rate Estimation
En Xu, Jingtao Ding, Zhiwen Yu +1
Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not directly observable on real…
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…
On the Accuracy Limits of Sequential Recommender Systems: An Entropy-Based Approach
En Xu, Jingtao Ding, Yong Li
Sequential recommender systems have achieved steady gains in offline accuracy, yet it remains unclear how close current models are to the intrinsic accuracy limit imposed by the da…
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…
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…
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…
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…
Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems
Zhi Sheng, Yuan Yuan, Yudi Zhang +2
Probabilistic forecasting is crucial for real-world spatiotemporal systems, such as climate, energy, and urban environments, where quantifying uncertainty is essential for informed…