让AI像程序员一样思考空间问题
现在的AI看3D场景就像近视眼——能认出物体，但搞不清它们之间的空间关系。研究者发现，问题出在AI调用工具的方式上：要么一次性写完所有代码（错了没法改），要么只能用固定按钮（不够灵活）。SpatialClaw让AI像程序员写代码一样，每步只写一行，随时能看到中间结果再调整下一步。在20个空间推理测试中，平均准确率59.9%，比之前最好的方法高出11个百分点。这不是你明天能用的功能，但它揭示了让AI真正理解三维世界的关键：不是给它更多数据，而是给它更灵活的思考方式。
📄 原文摘要(英文)
Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs). Tool-augmented agents attempt to address this by augmenting VLMs with specialist perception modules, yet their effectiveness is bounded by the action interface through which those tools are invoked. In this work, we study how the design of this interface shapes the agent's capacity for open-ended spatial reasoning. Existing spatial agents either employ single-pass code execution, which commits to a full analysis strategy before any intermediate result is observed, or rely on a structured tool-call interface that often offers less flexibility for freely composing operations or tailoring the analysis to each task. Both designs offer limited flexibility for open-ended, complex 3D/4D spatial reasoning. We therefore propose SpatialClaw, a training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw maintains a stateful Python kernel pre-loaded with input frames and a suite of perception and geometry primitives, letting a VLM-backed agent write one executable cell per step conditioned on all prior outputs, enabling the agent to flexibly compose and manipulate perception results and adapt its analysis to both intermediate text and visual observations and the demands of each problem. Evaluated across 20 spatial reasoning benchmarks spanning a broad range of static and dynamic 3D/4D spatial reasoning tasks, SpatialClaw achieves 59.9% average accuracy, outperforming the recent spatial agent by +11.2 points, with consistent gains across six VLM backbones from two model families without any benchmark- or model-specific adaptation.