We introduce Spatial Reasoning Models (SRMs), a framework to perform reasoning over sets of continuous variables via denoising generative models. SRMs infer continuous representations on a set of unobserved variables, given observations on observed variables. Current generative models on spatial domains, such as diffusion and flow matching models, often collapse to hallucination in case of complex distributions. To measure this, we introduce a set of benchmark tasks that test the quality of complex reasoning in generative models and can quantify hallucination. The SRM framework allows to report key findings about importance of sequentialization in generation, the associated order, as well as the sampling strategies during training. It demonstrates, for the first time, that order of generation can successfully be predicted by the denoising network itself. Using these findings, we can increase the accuracy of specific reasoning tasks from <1% to >50%.

翻译：我们提出了空间推理模型（SRMs），这是一个通过去噪生成模型对连续变量集合进行推理的框架。SRMs 在给定观测变量的观测值情况下，推断一组未观测变量的连续表示。当前在空间域上的生成模型，例如扩散模型和流匹配模型，在复杂分布情况下常常会坍缩为幻觉生成。为了衡量这一点，我们引入了一套基准测试任务，用于检验生成模型中复杂推理的质量，并可以量化幻觉现象。SRM 框架能够揭示关于生成过程中顺序化的重要性、相关顺序以及训练期间采样策略的关键发现。它首次证明，生成顺序可以成功由去噪网络自身预测。利用这些发现，我们可以将特定推理任务的准确率从 <1% 提升至 >50%。