Preprint in preparation

NewtPhys: Do Foundation Models Understand Newtonian Physics?

Sebastian Cavada, Soumava Paul, Tuan-Hung Vu, Andrei Bursuc, Raoul de Charette

Abstract

Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However, these benchmarks emphasize high-level events and lack the visual fidelity required to assess true low-level Newtonian understanding. We introduce NewtPhys a 4D physically annotated dataset built from multiview images of real-world scenes with physics-grounded simulations. The dataset provides dense, fine-grained annotations across timesteps — including 3D forces and amodal per-pixel quantities covering physics, tracking, semantics and geometry — bridging the gap between simplistic synthetic setups and realistic visual complexity. Using NewtPhys, we systematically evaluate 56 VLMs, {including 54 open-weight models and 2 closed-source frontier models}, and 10 VFMs and reveal limitations in low-level physics reasoning. Beyond benchmarking, our dataset enables future research in physics-grounded vision and the development of next-generation physics-aware evaluations. Code and datasets are available at https://astra-vision.github.io/NewtPhys.