Comment by j-pb
7 hours ago
The authors of the proof are invited to give many talks, and meet with other experts in the area. Workshops are set up to discuss the proof, as well as other recent developments.
problems are being solved autonomously by AI prompters who have no interest in the broader field itself once their initial target is "solved", and do not understand the AI output well enough to answer questions on the result
The value here seems to be the insights that the author of the proof gained, and the paths they took and maybe more importantly didn't take. Inviting only the human prompter to a talk on the paper is like inviting only the department chair, manager of the actual author.
The valuable part that Tao is feeling the absence of is the insight, and you can only get that from talking to the swarm of agents that developed the original proof with all of their context.
So to me it feels like we don't need Math 2.0, but Authorship 2.0. I want to "meet" the context that generated these proofs. I mean luckily these were not generated by faceless systems like a SAT solver, you can actually talk to it, but I'm not sure if we can step beyond our pride and grant the true authors of these proofs that recognition.
These systems don't have a genuine capacity of introspection, beyond just mining the conversation trace. When you're asking them why they did this or that, they are basically guessing anew from the outside, and are just as likely to hallucinate as they are to hit the right answer. These are mechanical systems which brute-force chains of various (re)combinations of techniques acquired from the training data. The true authors are all those who have contributed those techniques in the past.
I think that's debatable. Anthropic's interpretability research suggest models do have self-introspection ability, at least in the "J-Space": https://transformer-circuits.pub/2026/workspace/ ; and this private working/'introspection' space is distinct and distinguishable from the tokens they output (CoT tokens are output too).
This was how it was done in chemistry back in the day: release a cooking instruction. If it fails, visit the colleagues and give hints on what you meant.
The idea would be that you should not fiddle with the minds who try to independently evaluate your works, so that is not a feasible approach to truth seeking.
While in organic chemistry, this way, valid progress was made, you can always avoid a perpetuum mobile inventor and get conned.
The problem is that the reasoning traces are kept secret. OpenAI doesn't publish them because it would lead to distillation attacks from other AI companies.
The reasoning traces were traditionally keept secret by legacy mathematicians as well.
"When the architect completes a fine building, he removes the scaffolding." - Carl Friedrich Gauss
Legacy mathematicians can nontheless remember much of how they came up with their proof. Even though they (as Gauss says) usually don't publish this, they can still answer questions about it at conferences and workshops, or otherwise use the knowledge of the creation process to explain their proof. It's not a secret in the sense of OpenAI.
> problems are being solved autonomously by AI prompters who have no interest in the broader field itself once their initial target is "solved"
This is pretty much what a person that proivded patronage to a matematician used to be. API prompters are people who provide patronage for AI mathematicians.
You don't talk with them about the discoveries. About discoveries you should talk with who actually made them. Namely the LLMs.
Another analogy might by that you shouldn't expect to have interesting discussion about the essence of art with art producer.
Just thank them for the inference they covered and interact with the results instead.