A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models

Abstract:When a video model generates physically incorrect motion, did it fail to learn the correct motion, or did it learn it but fail to use it? We show the latter: the correct motion remains available inside the model and can still be made to control the generated video. We train on videos where red masses oscillate slowly and blue masses oscillate quickly, then test a red mass with fast observed motion. Even when the model generates slow motion in this conflicting case, a low-dimensional edit predicted from simple physical variables restores the correct fast motion. We call this ability causal writability. At fixed strength, we find a sharp depth boundary: the same edit changes the video before the boundary but not after it. This closure marks commitment for that write. The motion signal nevertheless remains, and a stronger downstream write can restore physical motion, while excessive gain overshoots. Early causal writability predicts which errors training later corrects: those errors are writable at more network depths than errors that persist. We reproduce both causal writability and its sharp closure in a pretrained 1.3B video model, supporting generality across model scale and training regime.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.15980 [cs.LG]

(or arXiv:2609.15980v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.15980 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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