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GPT-5.6 used a prompt to close a 30-year gap in convex optimization

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Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#4

Waiting for comments saying that LLMs can't produce anything new and general goalpost moving.

From the post lol

>So I wouldn't really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense to work on any low-hanging, or even medium-hanging (you know what I mean) fruit. We'll be needed for problems where actual novel approaches are needed.

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#5
post #4

Waiting for comments saying that LLMs can't produce anything new and general goalpost moving.

From the post lol >So I wouldn't really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense…

so it seems like The New Big Question In Math is

How's It Hanging, Brother?

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#6
post #4

Waiting for comments saying that LLMs can't produce anything new and general goalpost moving.

From the post lol >So I wouldn't really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense…

If knowledge is a Swiss cheese, LLMs can help fill the holes, but not make the cheese bigger.

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#7
What I'm feeling is that there's a need to study how to use AI well. I've seen professors using AI, and it was amazing. In that sense, I think AI prompt input will become stratified. In the past, implementation skills were very important, but these days, concepts feel more important this is one of those things.

It's not that AI brings equality, but rather that the output varies depending on how much background knowledge you have. You could call it a stratification of input

I'm starting to feel like there's no place left for programmers like me who focus on quickly churning out MVPs.

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#8
post #3

[flagged]

Lean is the Mizar here. For those who have no clue what this is about, Mizar [1] was an early automated theorem prover. Can't wait for HN to add AI features to explain concepts in the sideline, and autovoting.

[1] https://en.wikipedia.org/wiki/Mizar_system

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#9
post #7

What I'm feeling is that there's a need to study how to use AI well. I've seen professors using AI, and it was amazing. In that sense, I think AI prompt input will become stratified. In the past, implementation skills were very important, but these days, concepts feel more important this is one of those things. It's not that AI brings equality, but rather that the output varies depending on how much background knowle…

[dead]

Re: GPT-5.6 used a prompt to close a 30-year gap in convex optimization

#10
Two points:

- Hasn't been peer reviewed yet, so take with a grain of salt. This applies to all claimed proofs, not just AI-generated ones. Even humans hallucinate proofs too!

- The prompt is on page 27 here[1]. It is ten pages of advanced mathematics priming the model in the right direction, apparently informed by a year of prior research. That doesn't invalidate the result if it is genuine, but it is worth noting that this wasn't a matter of "ChatGPT, solve this unsolved problem. Make no mistakes." and required substantial domain expertise and human research beforehand.

[1]https://arxiv.org/pdf/2607.13335

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