大模型能从局部描述中构建全局空间认知，实现路径规划与空间推理。
Can LLMs Learn to Map the World from Local Descriptions?
- 基于局部位置关系整合，构建一致的全局空间布局
- 在模拟城市中成功推断未见地点间的关系并规划路径
- 适合研究空间推理、智能导航的AI开发者
大型语言模型（LLMs）在代码和数学等任务中表现出色，但其对结构化空间知识的内化能力仍待探索。本研究探讨了基于局部相对人类观察的LLMs，能否通过整合零散的关联描述，构建连贯的全局空间认知。重点关注两个核心方面：空间感知，即模型从局部位置关系推断一致的全局布局；空间导航，即模型从轨迹数据学习道路连通性并规划未连接点之间的最优路径。在模拟城市环境中的实验表明，LLMs不仅能泛化到未见的兴趣点（POIs）间空间关系，还展现出与真实世界空间分布对齐的潜在表征。此外，模型可从轨迹描述中学习道路连通性，实现精准路径规划与动态空间感知。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in tasks such as code and mathematics. However, their potential to internalize structured spatial knowledge remains underexplored. This study investigates whether LLMs, grounded in locally relative human observations, can construct coherent global spatial cognition by integrating fragmented relational descriptions. We focus on two core aspects of spatial cognition: spatial perception, where models infer consistent global layouts from local positional relationships, and spatial navigation, where models learn road connectivity from trajectory data and plan optimal paths between unconnected locations. Experiments conducted in a simulated urban environment demonstrate that LLMs not only generalize to unseen spatial relationships between points of interest (POIs) but also exhibit latent representations aligned with real-world spatial distributions. Furthermore, LLMs can learn road connectivity from trajectory descriptions, enabling accurate path planning and dynamic spatial awareness during navigation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。