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UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

106 upvotes · 27 AUG 2026 · Tianjie Ju, Zheng Wu, Yueqing Sun et al.

This paper explores how large language models can turn local observations of a city into reliable actions, and whether these models can sustain goal-directed behavior in complex urban environments. Practitioners in AI/ML and urban planning might care about the limitations and potential of current models in navigating real-world cities.

multimodal large language modelsurban agencyspatial reasoningopen-ended environmentscontinual learning