Xingchen Zou, Yuhao Yang, Zheng Chen, Xixuan Hao, Yiqi Chen, Chao Huang, Yuxuan Liang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Real-world data combines structured (e.g., graph connections) and unstructured (e.g., text, visuals) formats, capturing explicit relationships (e.g., social links) and implicit semantic interdependencies (e.g., knowledge graphs).We propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive (e.g., node classification) and generative (e.g., text generation) tasks.GraphAgent integrates three components: (i) a Graph Generator Agent creating knowledge graphs for semantic dependencies; (ii) a Task Planning Agent interpreting user queries and formulating tasks via self-planning; and (iii) a Task Execution Agent automating task execution with tool matching.These agents combine language and graph language models to reveal complex relational and semantic patterns.Extensive experiments on diverse datasets validate GraphAgent's effectiveness in graph-related predictive and text generative tasks.

Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regional samples is essential for enhancing management and sustainability in urban areas and human settlements. Current inference methods typically rely on spatial interpolation based on the assumption of spatial continuity, which does not adequately address the complex variations present within regional spaces. In this paper, we present GeoHG, the first space-aware socioeconomic indicator inference method that utilizes a heterogeneous graph-based structure to represent geospace for non-continuous inference. Extensive experiments demonstrate the effectiveness of GeoHG in comparison to existing methods, achieving an R2 score exceeding 0.8 under extreme data scarcity with a masked ratio of 95%. The code and data are available at https://github.com/CityMind-Lab/GeoHG.

Climate change and rapid urbanization have led to the Urban Heat Island (UHI) effect, resulting in higher temperatures in metropolitan areas and negatively impacting urban communities. Accurate UHI forecasting is crucial for identifying high-risk periods and locations, especially in cities with vulnerable populations. Current methods are limited by data granularity and inadequate modeling of regional thermodynamics, which affects both accuracy and spatio-temporal granularity. In this paper, we propose DeepUHI, a data-driven context-aware framework for modeling local thermodynamics based on the heat equation, alongside the SeoulTemp dataset, the first multi-modal dataset for UHI effect predictions at the street level. Our framework utilizes a heat decomposition method to represent urban thermodynamics through thermodynamic cycles and thermal flows, effectively integrating urban environmental data. Extensive experiments show that our framework improves accuracy in UHI effect prediction and warning tasks, outperforming leading models. We have integrated DeepUHI into our SeoUHI platform to provide hourly street-level UHI forecasting for Seoul. The code, platform, and dataset are accessible at https://github.com/CityMind-Lab/DeepUHI.

Spatio-Temporal Foundation Models (STFMs) promise zero/few-shot generalization across various datasets, yet joint spatio-temporal pretraining is computationally prohibitive and struggles with domain-specific spatial correlations. To this end, we introduce FactoST, a factorized STFM that decouples universal temporal pretraining from spatio-temporal adaptation. The first stage pretrains a space-agnostic backbone with multi-frequency reconstruction and domain-aware prompting, capturing cross-domain temporal regularities at low computational cost. The second stage freezes or further fine-tunes the backbone and attaches an adapter that fuses spatial metadata, sparsifies interactions, and aligns domains with continual memory replay. Extensive forecasting experiments reveal that, in few-shot setting, FactoST reduces MAE by up to 46.4% versus UniST, uses 46.2% fewer parameters, and achieves 68% faster inference than OpenCity, while remaining competitive with expert models. We believe this factorized view offers a practical and scalable path toward truly universal STFMs. The code will be released upon notification.

Building location embedding from web-sourced satellite imagery has emerged as an enduring research focus in web mining.However, most existing methods are inherently constrained by their reliance on discrete, sparse sampling strategies, failing to capture the essential spatial continuity of geographic spaces.Moreover, the presence of confounding factors in satellite images can distort the perception of actual objects, leading to semantic discontinuity in the embeddings.In this work, we propose SatCLE, a novel framework for Continuous Location Embeddings leveraging Satellite imagery.Specifically, to address the out-of-sample query challenge of spatial continuity, we propose a geospatial refinement strategy comprising stochastic perturbation continuity expansion and graph propagation fusion, which transforms discrete geospatial coordinates into a continuous space.To mitigate the effects of confounders on semantic continuity, we introduce causal refinement, integrating causal theory to localize and eliminate spurious correlations arising from the environmental context.Through extensive experiments, SatCLE shows state-of-the-art performance, exhibiting superior spatial coherence and semantic fidelity across diverse geospatial tasks.The source code is available at https://github.com/CityMind-Lab/SatCLE.