Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is developing representations that can efficiently learn and generalize physical dynamics from minimal data. Here we present Neural Force Field (NFF), a framework extending Neural Ordinary Differential Equation (NODE) to learn complex object interactions through force field representations, which can be efficiently integrated through an Ordinary Differential Equation (ODE) solver to predict object trajectories. Unlike existing approaches that rely on discrete latent spaces, NFF captures fundamental physical concepts such as gravity, support, and collision in continuous explicit force fields. Experiments on three challenging physical reasoning tasks demonstrate that NFF, trained with only a few examples, achieves strong generalization to unseen scenarios. This physics-grounded representation enables efficient forward-backward planning and rapid adaptation through interactive refinement. Our work suggests that incorporating physics-inspired representations into learning systems can help bridge the gap between artificial and human physical reasoning capabilities.

翻译：物理推理是人类的一项卓越能力，能够从有限经验中实现快速学习与泛化。尽管经过大量训练，当前的人工智能模型仍难以实现类似的泛化能力，尤其在分布外（OOD）场景中。这一局限源于其无法从观察中抽象出核心物理原理。关键挑战在于开发能够从少量数据中高效学习并泛化物理动态的表征方法。本文提出神经力场（NFF）框架，该框架通过扩展神经常微分方程（NODE），借助力场表征学习复杂物体交互作用，并可通过常微分方程（ODE）求解器高效积分以预测物体运动轨迹。与依赖离散潜在空间的现有方法不同，NFF在连续显式力场中捕捉重力、支撑、碰撞等基础物理概念。在三个具有挑战性的物理推理任务上的实验表明，仅需少量训练样本的NFF能够对未见场景实现强泛化能力。这种基于物理的表征支持高效的前向-后向规划，并能通过交互式精炼实现快速适应。我们的研究表明，将物理启发的表征融入学习系统有助于弥合人工与人类物理推理能力之间的差距。