Finding a specific landmark in a city-scale 3D scene with tens of millions of points usually means ranking every candidate. Urbex instead lets an agent explore like a person would. Title: Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes URL: 🏙️ The problem City-scale 3D scenes can span hundreds of thousands to tens of millions of points per scene, and existing ranking-based methods rely on precomputed instance proposals that don't scale. This work reframes urban localization as something inherently sequential and exploratory. 🔧 The approach A VLM agent uses three tools, landmark query, zoom-in, and oblique rendering, to explore a bird's-eye-view multi-view environment before committing a final bounding box. Trained with GRPO, the reward design combines IoU, center-distance shaping, and step penalties to optimize both accuracy and search efficiency. 📊 Results On the CityRefer benchmark, Hit@1 reaches up to 23.9% versus 14.2% for the baseline, while inference takes just 3.2 seconds versus 32.1 seconds, roughly a 10x speedup. Zero-shot transfer to an unseen urban scene still hits 27.2% Hit@1. 🔍 Remaining challenges Missing a named landmark drops Hit@1 by 10.8 points, while coordinate noise barely matters; and when many same-class objects cluster nearby, local success falls from 64.7% to 25.1%. #SpatialAI# #VLMAgents#