Newest papers

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

German

Bestehende Weltmodelle empfinden in der Regel eine Szene durch einen aktuellen visuellen Rahmen oder ein schmales Geschichtsfenster. Diese Beobachtungen können jedoch nicht die physikalischen Eigenschaften offenbaren, die die Übergangsdynamik bestimmen, wie z. B. die Massenverteilung eines Objekts, die gemeinsame Dämpfung oder die Oberflächenfraktion. Ohne diese physikalischen Faktoren für Weltmodelle zu kalibrieren, neigen Vorhersagen dazu, zu einem empirischen Vorgänger zurückzukehren und schwere Drift in neuartigen Testszenen zu induzieren. Zu diesem Zweck schlagen wir PhysiCalWorld vor, das actionbedingte Weltmodelle auf instanzspezifische Dynamik kalibriert, indem es in Kontext anruft, ohne Fein-Tuning oder analytische Physik

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange