There is soo much marketing bs around these things it drives me nuts. and it doesn't help that the large labs and credible individuals like denis use these terms. "world models" are video generator with contextual memory but that term is soo misplaced. when one thinks of a "world model" you expect the thing to be at least be physics engine driven from its foundation, not the other way around where everything is generated and assumed at best.

It's plato's cave, but in color! https://en.wikipedia.org/wiki/Allegory_of_the_cave / https://www.youtube.com/watch?v=1RWOpQXTltA&t=56s

> Calling it a world simulator is a marketing gimmick.

Yes, it should be called an AI Metaverse.

It does do a nice job of short term prediction. That's useful as a component of common sense.

why would you assume anything about "the next version"?

This should be interesting then: we’ll finally be able to assert whether time is deterministic and the future and past can be modelled/predicted (if you’ve seen the show you know what I mean)

  > But we feel Ok as long as the sequence doesn't horribly contradict the original image or sound. 

Is the error I pointed out not "horribly contradicting"?

  > That is the world model.

I would say that if it is non-physical[0] then it's hard to call it a /world/ model. A world is consistent and has a set of rules that must be followed.

I've yet to see a claimed world model that actually captures this behavior. Yet it's something every game engine[1] gets very well. We'd call it a bad physics engine if they made the same mistakes we see even the most advanced "world models" do.

This is part of why I'm trying to explain that visual quality is actually orthogonal. Even old Atari games have consistent world models despite being pixelated. Or think about Mario on the original NES. Even the physics breaking in that game are more edge cases and not the norm. But here, things like the lion's tail is not consistent even to a 2D world. I've never bought the explanation that teleporting in front of and behind the leg is an artifact of embedding 3D into 2D[2] because the issue is actually the model not understanding collision and occlusion. It does not understand how the sections relate to one another in the image.

The major problem with these systems is that they just hope that the physics is recovered through enough examples of videos. Yet if one studied physics (beyond your basic college courses) you'd understand the naïveté of that. It took a long time to develop physics due to these specific limitations. These models don't even have the advantage of being able to interact with the environment. They have no mechanisms to form beliefs and certainly no means to test them. It's essentially impossible to develop physics through observation alone

[0] with respect to the physics of the world being simulated. I want you distinguish real world physics from /a physics/

[1] a game physics engine is a world model. Which, as in stressing in [0], does not necessarily need follow real world physics. Mistakes happen of course but things are generally consistent.

[2] no video and almost no game is purely 2D. They tend to have backgrounds which places some layering but we'll say 2D for convenience and since we have a shared understanding

I'm just trying to be a bit more political as it can be hard to communicate the issues. My first degree is actually in physics and I'll just say... over there "world model" implies something very different.

Edit: I said a bit more in the reply to the sibling comment. But we're probably on a similar page.

I'll remind you I am a ML researcher.

So, you need to say more. Or at least give me some reason to believe you rather than state something as an objective truth and "just trust me". In the long response to a sibling I state more precisely why I have never bought this common conjecture. Because that's what it is, conjecture.

So give me at least some reason to believe you. Because you have neither logos nor ethos. Your answer is in the form of ethos, but without the critical requisites.

  > poorly defined.

Poorly defined is not the same as undefined. There are bounds and we have a decent understanding of what this means. Not having the details all worked out is not the same. Though that lack of precision is being used to get away with more slop.

  > I need to find the paper that deepmind produced

I've seen that paper and the results pretty close to the action. I've even personally talked with people that worked on that paper. It very frequently "forgets" what is outside its view and it very frequently performs non-physically consistent actions. When you evaluate those models don't just try standard things, do weird things. Like keep trying to extend the grabber arm and it shouldn't jump to other parts of the screen.

  > The problem is that its not really a world model, its just image gen.

Yes, that was my point. Since you agree I'm not sure why you're disagreeing.

Correct. The fact that AI is a black box means we can easily imagine anything we want happening within that box. Or perhaps the more accurate way to say it - AI companies can convince investors of amazing magic happening within that box. With LLMs, we anthropomorphize and imagine it’s thinking. With video models, they’re now trying to convince us that it understands the world. None of these things are true. It’s all an illusion.

Here's a recent paper showing that models trained to generate videos develop strong geometric representations and understanding:

https://arxiv.org/abs/2512.19949

You need to train a decoder either end to end or conditioned on latents.

In either case the impressiveness of that decoder can be far removed from the effectiveness of your world model or involve no world model at all