S-Agent：空间工具使用能够激发与空间智能相关的推理能力
S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence
摘要
在现实世界中，空间智能需要针对不断变化的3D环境进行推理。然而，现有的视觉语言模型和工具辅助的智能体大多仍然依赖于基于孤立视觉信息的静态、无状态推理方式。我们提出了\textsc{S-Agent}这一空间工具使用智能体模型，它能够用于理解和推理连续的多视图图像与视频信息。通过将空间推理视为时空层面的证据积累过程，而非基于单个帧的预测，S-Agent能够将空间感知转化为以场景为中心的认知方式，而非以帧为中心的识别方式。具体来说，S-Agent将视觉语言模型视为一个语义规划器，负责决定需要哪些证据；而一系列空间工具和专家则负责将物体从2D空间转化为3D几何信息，并将这些信息整合为高级的空间知识（如计数、测量、方向及相对位置等）。此外，时间记忆机制也不可或缺，包括用于维持场景状态的场景记忆，以及用于积累推理上下文的智能体记忆，这些机制使得不同帧之间的证据能够相互关联，从而实现有效的推理过程。在多视图和视频空间推理测试中的实验表明，S-Agent能够在无需训练的情况下，显著提升开源和闭源视觉语言模型的性能。除了在推理过程中进行的增强外，对S-Agent生成的S-300K空间轨迹进行有监督微调后，得到的S-Agent-8B模型则是一种高效的空间智能体，其性能明显优于同规模baseline模型（如Qwen3-VL-8B），同时也能够与先进的闭源模型（如GPT-5.4和Gemini 3）相媲美。
English Abstract
Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inference from isolated visual observations. We introduce \textsc{S-Agent}, a spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos. By formulating spatial reasoning as spatio-temporal evidence accumulation rather than isolated frame-level prediction, S-Agent reshapes spatial perception into scene-centric understanding beyond frame-centric recognition. Specifically, S-Agent casts the VLM as a semantic planner that decides what evidence is needed, while a hierarchy of spatial tools and experts grounds objects in 2D, lifts them into 3D geometric evidence, and aggregates this evidence into high-level spatial knowledge (e.g., counting, measurement, orientation, and relative position). Additionally, a temporal memory mechanism, including Scene Memory for maintaining the evolving scene state and Agent Memory for accumulating reasoning context, enables evidence integration across frames and reasoning steps. Comprehensive experiments on multi-view and video spatial reasoning benchmarks show that S-Agent consistently improves both open-source and closed-source VLMs in a training-free manner. Beyond inference-time augmentation, supervised fine-tuning (SFT) on S-Agent-generated spatial trajectories S-300K yields S-Agent-8B, a compact spatial agent that significantly surpasses similar-scale baselines (e.g., Qwen3-VL-8B) and performs comparably to advanced closed-source models (e.g., GPT-5.4 and Gemini 3).