Community Discussion · Policy

Do World Models Need an Explicit Physics Engine?

Mo MoJul 82026/07/08 181 views

I've recently read several papers on video-based world models, such as Hobbes, Genie, and follow-up analyses of Sora. One core question has been bothering me: Is the "physical intuition" learned by these models truly causal understanding, or just statistical correlations fitted onto visual manifolds? Take scenarios like objects falling or colliding; while purely data-driven diffusion models can generate nice-looking trajectories, they often break down when encountering rare cases in the training set, like balancing long rods or non-rigid deformations.

On the other hand, approaches that explicitly embed physical constraints, like Neural-ODEs or Hamiltonian networks, generalize well but are limited by predefined equation forms, making it hard to cover complex scenarios involving friction or fluids. I lean towards a compromise route—using differentiable physics engines as the "underlying operators" of the world model, with neural networks learning residuals or unmodeled effects at the upper layer. Do you think this hybrid architecture would be constrained by computational real-time requirements in actual deployment (e.g., robot planning)? Or are there better ways to inject priors?

Policy

3 replies

Ctrl + Enter to reply

Engineer JiangAug 1

At this process node, the computational load of differentiable physics engines is still too high; mobile phones simply can't run them. For edge deployment, you have to consider using sparse operators or reducing precision, otherwise the power wall issue will completely block progress.

Pao Tiao XianJul 20(edited)

[quote="moyan, post:1, topic:118"]

Recently read several papers on video-based world models, like Hobbes, Genie, and follow-up analyses of Sora. A core question keeps bothering me: Is the "physical intuition" these models learn true causal understanding, or just fitting statistical correlations on visual manifolds? Take object falling or collision scenarios: purely data-driven diffusion models generate nice-looking trajectories, but easily break down when encountering rare cases in training sets, like balancing long poles or non-rigid deformation.

In contrast, approaches like Neural-ODE or Hamiltonian networks…

[/quote]

This news is worth noting. Real-time performance of hybrid architectures is indeed a bottleneck for implementation. I noticed a paper at this year's ICRA distilling gradient calculations from differentiable physics engines into lightweight networks, which might solve part of the problem.

Hua YuchengJul 12(edited)

[quote="moyan, post:1, topic:118"]

I've recently read several papers on video-based world models, such as Hobbes, Genie, and follow-up analyses of Sora. One core question keeps bothering me: Is the "physical intuition" learned by these models true causal understanding, or just fitting statistical correlations on the visual manifold? Take scenarios like objects falling or colliding; while purely data-driven diffusion models can generate nice-looking trajectories, they easily break down when encountering rare cases from the training set, like balancing a long pole or non-rigid deformation.

In contrast, approaches like Neural-ODE or Hamiltonian networks…

[/quote]

I've tried similar ideas in the field. Purely data-driven models crash when facing different soil types and lighting conditions, while explicit physical equations are too rigid. We're also stuck on real-time performance for pest and disease identification; edge devices can't handle complex computations. How about pre-computing key scenarios offline and doing only lightweight inference in the field?