在结构中思考：评估约束空间中的空间智能
Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces
AI总结：
提出SSI-Bench基准，通过结构约束下的空间推理任务评估视觉语言模型的空间智能，发现模型与人类存在巨大差距。
摘要：
空间智能对视觉语言模型（VLM）至关重要，然而许多以场景为中心的基准评估的是无约束环境，其中单个图像可能允许多种合理的3D解释。我们引入了SSI-Bench，一个用于约束空间中结构中心空间推理（SCSR）的VQA基准。它基于复杂的真实世界3D结构，利用几何、拓扑和物理可行性方面的结构约束，使组件关系从视觉证据中更加确定。该基准包含1000个涵盖几何和拓扑推理的排序问题，其中正确的排序需要解决所有候选对象的3D关系，对空间理解提出了更强的要求。它通过完全以人为中心的流程创建，包括超过400研究员小时的图像整理、组件标注和问题设计。评估31个VLM揭示了与人类的巨大差距：最好的开源模型达到22.2%的准确率，最强的闭源模型达到33.6%，而人类得分为91.6%。进一步的结果表明，思维链推理仅带来微小的提升，错误分析揭示了当前模型在约束空间中空间理解的根本局限性。项目页面：https://ssi-bench.github.io。
英文摘要：
Spatial intelligence is crucial for vision--language models (VLMs), yet many scene-centric benchmarks evaluate unconstrained environments where a single image may admit multiple plausible 3D interpretations. We introduce SSI-Bench, a VQA benchmark for Structure-Centric Spatial Reasoning (SCSR) in constraint-governed spaces. Built from complex real-world 3D structures, it uses structural constraints from geometry, topology, and physical feasibility to make component relations more determinate from visual evidence. The benchmark contains 1,000 ranking questions spanning geometric and topological reasoning, where correct ordering requires resolving all candidate-wise 3D relations, imposing stronger demands on spatial understanding. It is created through a fully human-centered pipeline with over 400 researcher-hours of image curation, component annotation, and question design. Evaluating 31 VLMs reveals a large gap to humans: the best open-source model achieves 22.2% accuracy and the strongest closed-source model reaches 33.6%, while humans score 91.6%. Further results show that chain-of-thought reasoning brings only marginal gains, and error analysis reveals fundamental limitations in current models' spatial understanding within constraint-governed spaces. Project page: https://ssi-bench.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
