An AI that can turn the right corner doesn't mean it can navigate an entire city. UrbanGround measures exactly that gap, at real scale in Hong Kong. UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City 🏙️ Overview MLLMs have shown impressive local spatial skills — visual recognition, short-range movement, VQA — but whether those skills translate into sustained city-scale action was untested. UrbanGround is a physically-simulated replica of Hong Kong, built from geospatial data (OSM, satellite maps) in Unity, that evaluates MLLM spatial agency through 810 manually-verified task instances arranged across a five-level evaluation ladder. 🔍 The Problem Existing spatial reasoning benchmarks are mostly small-scale, synthetic, and short-range. None test the compounding of local decisions into kilometers-long routes, nor robustness to dynamic changes like weather, road closures, or pedestrian crowds. ⚙️ Methodology A three-layer framework (geospatial / simulation / agent) gives models first-person vision and an interactive map interface. Tasks escalate in five levels: ・Level 1: Visual recognition, orientation, active exploration ・Level 2: Short/long-range and instruction-constrained navigation ・Level 3: Implicit destination inference from description ・Level 4: Multi-task scheduling and route optimization ・Level 5: Dynamic adaptation to closures and pedestrians 📊 Results Visual recognition scores 77–93% — relatively strong. Orientation judgment drops to 23–58%. Short-range navigation success (~70%) collapses to near-zero for long-range tasks. Weather and lighting cut QA accuracy by 5–20 points. Pedestrian collision rates exceed 75% across all models. GPT-5.5 and Kimi-K3 lead overall, but all models share the same failure modes at scale. The core finding: "Local abilities do not compose into sustained exploration." Agents move locally compliant routes but cannot maintain spatial estimates beyond visible scenes or revise plans when routes become invalid. #MLLMs# #EmbodiedAI#