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PhysiCalWorld: In-Context Physics Calibration for World Models

Yunhao Luo, He Wentao, Yinpei Dai, Hyelin Nam, Hongze Fu, Joyce Yue Chai, Nima Fazeli, Jeong Joon Park · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30125 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Los modelos mundiales existentes generalmente perciben una escena a través de un marco visual actual o de una ventana de historia estrecha. Sin embargo, estas observaciones pueden no revelar las propiedades físicas que determinan la dinámica de transición, como la distribución de masa de un objeto, la atenuación de la articulación o la fricción de la superficie. Sin calibrar estos factores físicos para modelos mundiales, las predicciones tienden a regresar hacia un precedente empírico e inducir un deslizamiento severo en escenas de pruebas novedosas. A este fin, proponemos PhysiCalWorld, que calibra los modelos mundiales condicionados por acción a la dinámica específica de la instancia a través de la prompting en contexto, sin fijar el ajuste ni modelar la física analítica. Nuestro método codifica un clip de interac

Machine translation (Meta M2M100); the English original follows.

Abstract

Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics properties that determine transition dynamics, such as an object's mass distribution, joint damping, or surface friction. Without calibrating these physics factors for world models, predictions tend to regress toward an empirical prior and induce severe drift in novel test scenes. To this end, we propose PhysiCalWorld, which calibrates action-conditioned world models to the instance-specific dynamics via in-context prompting, without fine-tuning or analytical physics modeling. Our method encodes an exploratory interaction clip collected within the test scene into a compact physics descriptor token, which grounds subsequent predictions in the scene's actual physical behavior. PhysiCalWorld is trained end-to-end with the latent prediction loss alone, without physics labels. At inference time, the descriptor token is extracted once and can be reused across rollouts with negligible compute overhead. We evaluate PhysiCalWorld across four environments covering diverse physics variations and show that it substantially improves rollout prediction accuracy and visual planning success rate. The learned descriptors also form a physically structured latent space and generalize beyond the physics ranges seen during training.

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Topics: Inference, Calibration, Physics beyond the Standard Model, Physics, Computer science, Space (punctuation)

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