arXiv Machine Learning June 5, 2026 By Zhaoqi Zhang, Miao Xie, Yi Li, Linyou Cai, Siqiang Luo, Gao Cong

CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

Read the original on arXiv Machine Learning →

arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jun 15

MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting

arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.

By Lilan Peng, Yandi Liu, Qingren Yao, Chongshou Li, Tianrui Li

llms

arXiv Machine Learning

Jun 18

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.

By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia

rag safety

arXiv Machine Learning

Jul 8

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

arXiv:2605. 07267v2 Announce Type: replace Abstract: Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time.

By Elahe Khatibi, Ziyu Wang, Saba A. Farahani, Di Huang, Hung Cao, Ramesh Jain, Amir M. Rahmani

benchmarks

arXiv AI

Jun 12

MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting

arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.

By Lilan Peng, Yandi Liu, Qingren Yao, Chongshou Li, Tianrui Li

llms

arXiv Machine Learning

Aug 26

Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks

arXiv:2505.11239v4 Announce Type: replace Abstract: Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning,...

By Wilson Wongso, Hao Xue, Flora D. Salim

agents benchmarks

arXiv Machine Learning

1d ago

Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports

The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.

By Liangkai Zhou, Susu Xu, Shuqi Zhong, Shan Lin

One email a morning, machine-written

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