SRM is a spatial reasoning framework based on a denoising generative model, used for inference tasks on sets of continuous variables. It gradually infers the continuous representation of these variables by assigning an independent noise level to each unobserved variable. This technique excels in handling complex distributions and effectively reduces hallucinations during the generation process. SRM demonstrates for the first time that denoising networks can predict the generation order, thus significantly improving the accuracy of specific inference tasks. The model was developed by the Max Planck Institute for Informatics in Germany and aims to advance research in spatial reasoning and generative models.