2 citations · 2 across the 7 of their papers we have counts for
7 papers
SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation
Yunqi Liu, Yang Zhang, Ruixing Zhang +4
Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequential generation remains chall…
Intent Speaks Louder: Controllable User Simulation Beyond Response Imitation
Bo Wang, Ruixing Zhang, Yunqi Liu +4
User simulators are widely used as scalable environments for training and evaluating interactive assistants. Generating the next user turn is inherently one-to-many: the same profi…
Think over Trajectories: Leveraging Video Generation to Reconstruct GPS Trajectories from Cellular Signaling
Ruixing Zhang, Hanzhang Jiang, Leilei Sun +3
Mobile devices continuously interact with cellular base stations, generating massive volumes of signaling records that provide broad coverage for understanding human mobility. Howe…
The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization
Ruixing Zhang, Zihan Liu, Leilei Sun +2
Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is…
Urban In-Context Learning: Bridging Pretraining and Inference through Masked Diffusion for Urban Profiling
Ruixing Zhang, Bo Wang, Tongyu Zhu +2
Urban profiling aims to predict urban profiles in unknown regions and plays a critical role in economic and social censuses. Existing approaches typically follow a two-stage paradi…
Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing
Ruixing Zhang, Liangzhe Han, Leilei Sun +3
User profiling and region analysis are two tasks of significant commercial value. However, in practical applications, modeling different features typically involves four main steps…
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
Ruixing Zhang, Liangzhe Han, Boyi Liu +2
Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a cha…