We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for
assessing machine physical commonsense. ContPhy complements existing physical
reasoning benchmarks by encompassing the inference of diverse physical
properties, such as mass and density, across various scenarios and predicting
corresponding dynamics. We evaluated a range of AI models and found that they
still struggle to achieve satisfactory performance on ContPhy, which shows that
the current AI models still lack physical commonsense for the continuum,
especially soft-bodies, and illustrates the value of the proposed dataset. We
also introduce an oracle model (ContPRO) that marries the particle-based
physical dynamic models with the recent large language models, which enjoy the
advantages of both models, precise dynamic predictions, and interpretable
reasoning. ContPhy aims to spur progress in perception and reasoning within
diverse physical settings, narrowing the divide between human and machine
intelligence in understanding the physical world. Project page:
this https URL

我们引入了连续物理数据集（ContPhy），这是一个用于评估机器物理常识的新型基准。通过涵盖不同场景中的多样化物理属性推理以及对应的动力学预测，ContPhy 补充了现有的物理推理基准。我们评估了一系列 AI 模型，并发现它们在 ContPhy 上仍然难以达到令人满意的性能，这表明当前的 AI 模型在连续物体方面仍然缺乏物理常识，尤其是软体，并证明了该数据集的价值。我们还引入了一种基于粒子的物理动力学模型与最近的大型语言模型相结合的神谕模型（ContPRO），该模型结合了两种模型的优势，即精确的动力学预测和可解释的推理。ContPhy 旨在促进在不同物理场景中的感知和推理的进步，缩小人类和机器智能在理解物理世界方面的差距。项目页面：这个 https URL