用去噪模型实现复杂空间推理，解决生成幻觉问题
Spatial Reasoning with Denoising Models
- 通过去噪生成模型对连续变量集进行空间推理
- 将特定推理任务准确率从不足1%提升至超50%
- 可自学习生成顺序，适合需要精确空间建模的场景
我们提出空间推理模型（SRMs），一种基于去噪生成模型对连续变量集合进行推理的框架。在观测到部分变量的情况下，SRMs能推断未观测变量的连续表示。当前的空间生成模型（如扩散模型、流匹配模型）在复杂分布下常出现幻觉。为此，我们设计了一套基准任务，用于评估生成模型的复杂推理质量并量化幻觉程度。SRM框架揭示了生成过程中的序列化重要性、生成顺序及训练时采样策略的影响。首次证明去噪网络可自主预测生成顺序。基于这些发现，特定推理任务的准确率从低于1%提升至超过50%。项目网站提供额外视频、代码和基准数据集：https://geometric-rl.mpi-inf.mpg.de/srm
原文摘要 · Abstract (English)
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%. Our project website provides additional videos, code, and the benchmark datasets: https://geometric-rl.mpi-inf.mpg.de/srm
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