Lost on Campus: Evaluating Embodied Spatial Reasoning of Vision-Language Models in the Wild

Embodied Spatial Reasoning (ESR) is central to deploying Vision-Language Models (VLMs) in real-world embodied tasks, yet remains inadequately evaluated. Existing benchmarks primarily assess spatial reasoning in static images or synthetic indoor environments, where semantic shortcuts often suffice, making it difficult to faithfully evaluate spatial reasoning capabilities in the wild. In light of this, we present Lost on Campus, a benchmark for evaluating ESR in large-scale real-world outdoor 3D environments reconstructed by 3D Gaussian Splatting. Our benchmark introduces a unified reasoning-action evaluation framework that seamlessly integrates diagnostic QA for isolated reasoning with closed-loop interactive navigation for active reasoning under multimodal instructions. To enable fine-grained diagnosis, we systematically decompose ESR into six fundamental capabilities: action grounding, spatial foresight, metric awareness, goal-directed planning, global localization, and spatio-temporal consistency. Extensive experiments reveal that: (i) Compared to indoor settings, real-world outdoor environments impose substantially higher demands on spatial reasoning, where existing models exhibit poor performance; (ii) VLMs still struggle with fine-grained visual alignment, with failures in spatial foresight and self-aware localization emerging as primary bottlenecks; (iii) Improving multimodal interaction capabilities and long-term spatial reasoning is crucial for advancing embodied intelligence. These findings highlight that faithful evaluation of ESR demands benchmarks tightly coupling perception, reasoning and action within realistic and continuous environments.