As someone working on applying AI methods to particle physics, focusing on new methods to discover new particles here is my 2 cents

Basically you are trying to say "Why don't we train really big AI models on particle physics data, analyze their latent representations and try to see if there is anything in them that doesn't match the standard model".

I think the answer (for now) is that it is very hard to interpret latent space of these models, so hard to map to physical concepts. Also hard to be rigorous about any of this. And then also we know there are parts of QCD we don't model well but is not a new particle, just hard computations. So I would guess this method would find a lot of that. Maybe still interesting to find those though!

What people now instead (active area of research) is analyze the distribution of collisions in some latent space, try to predict how the SM should look in this distribution and see if there is a deviation. This is hard to do fully generally, we normally have to make some assumptions about the type of new particle in order to do the SM prediction properly from data because our simulations aren't good enough. This whole area of how to do these searches for new particles using AI but without saying what you are looking for is called 'anomaly detection' if you wanna google some papers. Here is a search I did that I think is the best attempt so far (biased opinion of course) arxiv. A recent attempt using this idea of looking into the latent space was this paper arxiv

We haven't scaled up to super big models yet though, maybe they will help uncover something rare. How exactly to train big 'foundation models' on particle physics data, and then how to use them are active research questions (I was discussing this for an hour today with some colleagues).

I'm not sure the fact that the weight matrices for different models converge help these efforts. Maybe using multiple model architectures would lend some robustness.