Training a video model to prefer physically plausible results is not the same as giving it a simulator. The distinction matters whenever a model is described as understanding the physical world.
LingBot-Video uses a reward system that includes physical rationality and task completion alongside more familiar video criteria. That can push training away from obviously impossible motion. It does not introduce explicit mass, friction, collision geometry, or a numerical integrator. The model is still learning regularities from data and feedback.
I would test this with controlled changes rather than a collection of attractive clips. Keep the scene fixed, vary one initial condition, and check whether the outcome changes in the expected direction. Even then, success would show better learned dynamics, not prove that the system contains a general physics engine. The wording may sound picky, but it keeps a training objective from being mistaken for an implementation.
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