While large language models have become the prevailing approach for agentic reasoning and planning, their success in symbolic domains does not readily translate to the physical world. Spatial intelligence, the ability to perceive 3D structure, reason about object relationships, and act under physical constraints, is an orthogonal capability that proves important for embodied agents. Existing surveys address either agentic architectures or spatial domains in isolation. None provide a unified framework connecting these complementary capabilities. This paper bridges that gap. Through a thorough review of over 2,000 papers, citing 742 works from top-tier venues, we introduce a unified three-axis taxonomy connecting agentic capabilities with spatial tasks across scales. Crucially, we distinguish spatial grounding (metric understanding of geometry and physics) from symbolic grounding (associating images with text), arguing that perception alone does not confer agency. Our analysis reveals three key findings mapped to these axes: (1) hierarchical memory systems (Capability axis) are important for long-horizon spatial tasks. (2) GNN-LLM integration (Task axis) is a promising approach for structured spatial reasoning. (3) World models (Scale axis) are essential for safe deployment across micro-to-macro spatial scales. We conclude by identifying six grand challenges and outlining directions for future research, including the need for unified evaluation frameworks to standardize cross-domain assessment. This taxonomy provides a foundation for unifying fragmented research efforts and enabling the next generation of spatially-aware autonomous systems in robotics, autonomous vehicles, and geospatial intelligence.

翻译：尽管大型语言模型已成为智能体推理与规划的主流方法，其在符号领域的成功并不能直接迁移到物理世界。空间智能——即感知三维结构、推理物体关系以及在物理约束下行动的能力——是一种正交的能力，被证明对具身智能体至关重要。现有综述要么单独讨论智能体架构，要么单独讨论空间领域，均未提供连接这两种互补能力的统一框架。本文旨在弥合这一鸿沟。通过对超过2000篇论文的系统综述（引用顶级会议/期刊的742篇文献），我们提出了一个连接智能体能力与跨尺度空间任务的统一三维分类体系。关键的是，我们区分了空间基础（对几何与物理的度量理解）与符号基础（将图像与文本关联），论证了仅凭感知并不能赋予智能体行动能力。我们的分析揭示了映射到这三个维度的三项核心发现：（1）分层记忆系统（能力维度）对长时程空间任务至关重要；（2）GNN-LLM融合（任务维度）是结构化空间推理的有效途径；（3）世界模型（尺度维度）对于实现从微观到宏观空间尺度的安全部署不可或缺。最后，我们指出了六大核心挑战并展望了未来研究方向，包括建立统一评估框架以实现跨领域标准化评估的需求。本分类体系为整合碎片化的研究提供了基础，将推动机器人、自动驾驶和地理空间智能等领域下一代空间感知自主系统的发展。