用生成去噪模型实现任意领域连续变量的空间推理。
Spatial Reasoners for Continuous Variables in Any Domain
- 基于去噪生成模型构建空间推理框架，支持任意数据域映射。
- 统一接口管理不同模型架构与推断策略，降低研究门槛。
- 适合跨领域连续变量建模与复杂分布推理的研究者使用。
我们提出 Spatial Reasoners，一个用于在任意领域中对连续变量进行空间推理的软件框架，采用生成去噪模型实现。去噪生成模型因能有效从复杂高维分布中采样，已成为图像生成的主流方法。近期，这类模型开始被探索用于多连续变量的推理任务。然而，构建此类生成推理系统需应对多种去噪形式、采样器和推断策略，开发成本高。本框架旨在简化研究工作，提供易用接口，支持任意数据域的变量映射、生成模型范式及推断策略的灵活配置。Spatial Reasoners 已开源，项目地址为 https://spatialreasoners.github.io/。
原文摘要 · Abstract (English)
We present Spatial Reasoners, a software framework to perform spatial reasoning over continuous variables with generative denoising models. Denoising generative models have become the de-facto standard for image generation, due to their effectiveness in sampling from complex, high-dimensional distributions. Recently, they have started being explored in the context of reasoning over multiple continuous variables. Providing infrastructure for generative reasoning with such models requires a high effort, due to a wide range of different denoising formulations, samplers, and inference strategies. Our presented framework aims to facilitate research in this area, providing easy-to-use interfaces to control variable mapping from arbitrary data domains, generative model paradigms, and inference strategies. Spatial Reasoners are openly available at https://spatialreasoners.github.io/
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