Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern generative or unified multimodal models also possess generative spatial intelligence (GSI), the ability to respect and manipulate 3D spatial constraints during image generation, and whether such capability can be measured or improved. We introduce GSI-Bench, the first benchmark designed to quantify GSI through spatially grounded image editing. It consists of two complementary components: GSI-Real, a high-quality real-world dataset built via a 3D-prior-guided generation and filtering pipeline, and GSI-Syn, a large-scale synthetic benchmark with controllable spatial operations and fully automated labeling. Together with a unified evaluation protocol, GSI-Bench enables scalable, model-agnostic assessment of spatial compliance and editing fidelity. Experiments show that fine-tuning unified multimodal models on GSI-Syn yields substantial gains on both synthetic and real tasks and, strikingly, also improves downstream spatial understanding. This provides the first clear evidence that generative training can tangibly strengthen spatial reasoning, establishing a new pathway for advancing spatial intelligence in multimodal models.

翻译：空间智能对于多模态大语言模型至关重要，然而当前基准测试主要仅从理解角度评估该能力。我们探究现代生成式或统一多模态模型是否也具备生成式空间智能（GSI），即在图像生成过程中遵守并操控三维空间约束的能力，以及这种能力是否可被测量或提升。我们提出了GSI-Bench，这是首个通过空间接地图像编辑来量化GSI的基准测试。它包含两个互补组成部分：GSI-Real，一个基于三维先验引导生成与过滤流程构建的高质量真实世界数据集；以及GSI-Syn，一个具有可控空间操作与全自动标注的大规模合成基准测试。结合统一评估协议，GSI-Bench能够实现可扩展的、与模型无关的空间合规性与编辑保真度评估。实验表明，在GSI-Syn上微调统一多模态模型可在合成与真实任务上取得显著提升，且引人注目的是，这也改进了下游空间理解能力。这首次提供了明确证据，证明生成式训练能够切实增强空间推理能力，为推进多模态模型中的空间智能开辟了新途径。