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PhysiCalWorld: In-Context Physics Calibration for World Models

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

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

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#202 most recent of 960 cs.AI papers we have recorded · ↑ newer: Think Harder Where It Matters: Item-Level Adaptive Recursion for Seque · ↓ older: Environment Duels: A Microcosm of LLM Co-Evolution
Cite this page: PhysiCalWorld: In-Context Physics Calibration for World Models: the #202 most recent of 960 cs.AI papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/physicalworld-in-context-physics-calibration-for-world-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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