从文本到空间：在网格世界导航任务中LLM对抽象空间模型的映射

理解大型语言模型(LLMs)如何表示和推理关于空间信息的方式对于构建能够导航真实和模拟环境的强大代理系统至关重要。在本工作中，我们研究了不同文本表示对LLM性能和内部激活的影响，在一个网格世界导航任务中。通过对各种模型大小进行评估，我们检查了用于编码空间信息的格式如何影响决策。我们的实验表明，笛卡尔空间表示 consistently yield higher success rates and path efficiency, with performance scaling markedly with model size. Moreover, probing LLaMA-3.1-8B revealed subsets of internal units, primarily located in intermediate layers, that robustly correlate with spatial features, such as the position of the agent in the grid or action correctness, regardless of how that information is represented, and are also activated by unrelated spatial reasoning tasks. This work advances our understanding of how LLMs process spatial information and provides valuable insights for developing more interpretable and robust agentic AI systems.