UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the agent starts to move. In this paper, we investigate how far current MLLM agents can turn local urban perception into reliable action in a complicated real-scale city. We propose UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data. UrbanGround supports closed-loop interaction from a first-person view and provides an interactive map for navigati
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- PossiblePossibly related (embedding) · 54%Simulate real-world places with Project Genie and Street View →
- PossiblePossibly related (embedding) · 52%A look at spatial intelligence and world models - InfoWorld →
- FuzzySimilar title/name (fuzzy) · 84%mudler/LocalAI →
“Fuzzy title match (0.92): “UrbanGround: From Local Perception to Spatial Agency in a Re” ≈ “mudler/LocalAI””
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- LinkedLinked via arxiv author · 85%Tianjie Ju →
“UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City”