Large Language Models excel at linguistic tasks but lack the inner geospatial capabilities needed for time-critical disaster response, where reasoning about road networks, continuous coordinates, and access to essential infrastructure such as hospitals, shelters, and pharmacies is vital. We introduce GeoResponder, a framework that instills robust spatial reasoning through a scaffolded instruction-tuning curriculum. By stratifying geospatial learning into different cognitive layers, we effectively anchor semantic knowledge to the continuous coordinate manifold and enforce the internalization of spatial axioms. Extensive evaluations across four topologically distinct cities and diverse tasks demonstrate that GeoResponder significantly outperforms both state-of-the-art foundation models and domain-specific baselines. These results suggest that LLMs can begin to internalize and generalize geospatial structures, pointing toward the future development of language models capable of supporting disaster response needs.

翻译：大型语言模型在语言任务上表现出色，但缺乏时间关键型灾害响应所需的内在地理空间能力，而此类响应中对道路网络、连续坐标以及医院、避难所和药房等关键基础设施的可达性进行推理至关重要。我们提出了GeoResponder框架，该框架通过一个分阶段的指令微调课程，注入了强大的空间推理能力。通过将地理空间学习分层为不同的认知层级，我们有效地将语义知识锚定到连续坐标流形上，并强制空间公理的内化。在四个拓扑结构不同的城市和多样化任务上进行的大量评估表明，GeoResponder显著优于最先进的基础模型和特定领域基线模型。这些结果表明，大语言模型可以开始内化和泛化地理空间结构，为未来开发能够支持灾害响应需求的语言模型指明了方向。