Yeah, but nah, not yet at least, not without a lot of holding an AI's hair back as it expels 'thoughts'.
Overall, the experience of doing mathematics with GPT-5 was mixed.
It felt very similar to working with a junior assistant at the beginning of a new project: exploring directions, formulating hypotheses, searching for counterexamples, and progressively adjusting statements.
The AI showed a genuine ability to follow guided reasoning, to recognize its mistakes when pointed out, to propose new research directions, and to never take on the task. However, this only seems to support incremental research, that is, producing new results that do not require genuinely new ideas but rather the ability to combine ideas coming from different sources.
At first glance, this might appear useful for an exploratory phase, helping us save time.
In practice, however, it was quite the opposite: we had to carefully verify everything produced by the AI and constantly guide it so that it could correct its mistakes.
soldtm•5mo ago
I’m not explaining how it was built not yet. What I am doing is asking: Can you figure out how the result was reached?
Full paper is here: https://doi.org/10.5281/zenodo.17042143
If you’re deep in the weeds of QFT, lattice gauge, or spectral gaps, you’ll know what to look for. If not, maybe the framework still sparks something.
This isn’t bait. It’s a real, working answer. But the starting point that’s the part you get to reconstruct.