While Vision-Language Models (VLMs) excel at semantic understanding, they struggle to comprehend 3D spatial relationships from limited views. Their reliance on implicit geometric encoding often leads to severe hallucinations and inconsistencies in spatial reasoning tasks. To address this, we introduce GeoMind, a model-then-reason framework that employs a single LLM to autoregressively generate an explicit Geometric Description Language (GDL) map, serving as a grounded context to derive the final answer. This intermediate GDL map provides an explicit and queryable world representation. Leveraging this explicit representation, we enforce a strict referential constraint, compelling the model to ground reasoning solely on the instantiated entities to ensure referential integrity and auditability. Specifically, we lift multi-view observations into object-centric tokens using frozen geometric priors and instance masks. The LLM is trained via a two-stage curriculum with programmatic supervision to generate the GDL map as a prerequisite for answering. On five spatial understanding benchmarks in both image and video settings, GeoMind delivers average accuracy gains of +6.9% (2B) and +9.8% (8B) over Qwen3-VL baselines. Our results suggest that explicit geometric grounding enables robust spatial reasoning without human annotation, providing a scalable and practical route to stronger spatial intelligence in large VLMs.