LLMs excel at linguistic tasks but lack the inner geospatial capabilities needed for time-critical disaster response, where reasoning about road networks, 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 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：大语言模型（LLMs）在语言任务中表现卓越，但在时效性灾害响应中缺乏内在的地理空间能力——而推理路网、坐标、以及医院、避难所、药店等关键基础设施的获取至关重要。我们提出GeoResponder框架，通过分层指令微调课程来注入稳健的空间推理能力。通过将地理空间学习划分为不同认知层级，我们将语义知识锚定至连续坐标流形，并强制内化空间公理。在四个拓扑结构迥异的城市及多样化任务上的广泛评估表明，GeoResponder显著优于现有最先进的基础模型与领域专用基线。这些结果提示，大语言模型已开始内化并泛化地理空间结构，为未来开发支持灾害响应需求的语言模型指明了方向。