For folks wondering whether to read or not, here is the conclusion from the paper verbatim

> We have demonstrated that a single transformer-based model can effectively learn and predict the dynamics of diverse physical systems without explicit physics-specific features, marking a significant step toward true Physics Foundation Models. GPhyT not only outperforms specialized architectures on known physics by up to an order of magnitude but, more importantly, exhibits emergent in-context learning capabilities—inferring new boundary conditions and even entirely novel physical phenomena from input prompts alone.

▲ godelski 3 days ago | parent [-]

Thanks, I haven't been able to give the paper a proper read, but are they're basing claims via results or the ability to recover physics equations?

Because those two things are very different. You can have models that make accurate predictions without having accurate models of "the world" (your environment, not necessarily the actual world)[0]. We can't meaningful call something a physics model (or a world model) without that counterfactual recovery (you don't need the exact laws of physics but you need something reasonable). After all, our physics equations are the most compressed forms or representing the information we're after.

I ask because this is a weird thing that happens in a lot of ML papers when approaching world models. But just looking at results isn't enough to conclude if a world is being modeled. Doesn't even tell you if that's self consistent, let alone counterfactual.

[0] classic example is the geocentric model. They made accurate predictions, which is why it stayed around for so long. It's not like the heliocentric model didn't present new problems. There was reason for legitimate scientific debate at the time but that context is easily lost to history.

▲ flwi 3 days ago | parent [-]

Hey author here. Your argument is completely valid, we only model physics implicitly and thus have no prove that the model "actually knows the physics". Practically, this might not matter much: If the model can predict the evolution of the system to a certain accuracy, the user won't care about the underlying knowledge. And even for modern physics (quantum / GR), we know we miss something and yet, the models we have are incredibly useful.

On a tangent, we cannot prove that LLMs actually know language, yet they can be incredibly useful. Of course, a true world model would be much nicer to have, I agree with that!

	▲	godelski 2 days ago \| parent [-]
		`> Practically, this might not matter much: If the model can predict the evolution of the system to a certain accuracyI'm` It sounds like you didn't actually read what I wrote then `> the user won't care about the underlying knowledge.` I hear this argument a lot and it's tiresome. No one here is not concerned with results. Why imply that's not my main concern? Read my example. People will care if you have a more complicated geocentric model. Geocentric was quite useful, but also quite wrong, distracting, and made many bad predictions as well as good ones. The point is that it is wrong and this always bounds your model to being wrong. The big difference is if you don't extract the rules your model derived then you won't know when or how your model is wrong. So yes, the user cares. Because the user cares about the results. This is all about the results... `> we cannot prove that LLMs actually know language` We or you? Those are very different things. Is it a black box because you can't look inside out because you didn't look inside? Because I think you'll find some works that do exactly what we're talking about here. And if you're going to make big talk about PINNs then you need to know their actual purpose. Like come on man, you're claiming a physics model. How can you claim a physics model without the physics?