然而，当涉及到空间推理任务时，LLMs的表现却显得力不从心，甚至在处理基本的空间任务时也遇到困难，例如地理解析和理解相对空间关系。这种差距在处理现实世界的空间推理任务时尤为明显，例如图1中所示的场景： ...

研究团队在纽约和迈阿密的旅游数据集上进行评估，表明Spatial-RAG在众多基线方法中脱颖而出。通过消融实验，团队发现，尽管移除某些模块会导致特定指标的下降，但整体准确性仍然得到保障。

为了突破这一瓶颈，研究人员推出了 Spatial Retrieval-Augmented Generation (Spatial-RAG)—— 一个革命性的框架，旨在增强 LLMs 在空间推理任务中的能力。

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