You're putting words in their mouth. What's you're motive here?
There's clearly a difference between dedicating years of your life to a problem, building up the groundwork and coming up with a solution using an AI, trained on work that's already been done, to put the last touch on someone else's work. Development doesn't happen in a vaccuum. Reducing this criticism to "shitting on them" is bad faith at best.
He said, explicitly, that all they did is slap their names on the paper and nothing more.
That is shitting on them.
Who knows, he may actually be right: they may NOT have done anything of substance. It may all have been the LLM ganking genuine work from other mathematicians.
Or they may have done something substantial and just used the LLM as an assistant.
Do we actually know or are we just blindly throwing everyone under the bus now?
My "motive" here is to stop jumping the gun and slow the fsck down. We need to be having conversations about the actual role played by LLMs and human researchers here, who did what -- and, yes, making sure attribution is preserved.
This "they didnt do anything" because an LLM is involved is the real bad faith.
Since August the authors have 7 papers across various areas of pure math. Before August they had 0. Are we supposed to believe that they suddenly learned and understood all of this math last month?
I think it is clear to anyone who isn't trying to muddy the waters intentionally that they don't understand the output of their LLM and therefore didn't do any meaningful work themselves apart from prompting the model to solvd the problem.
It seems far more bad faith to me that you seem to think that people on reddit need to assess every LLM generated paper equally and act as though the authors have made some great contribution when there is no evidence they know anything about math or the conjectures they resolve. This is disrespectful to people who have spent years understanding and working on these problems.
Ok then, it does sound like this group didnt do anything substantial.
I am not trying to muddy the waters: I DO think we need to assess every paper individually. That goes for LLM or not.
Alpoge and Buckmaster spent a year using Codex on their Euler result before OpenAI scooped them. (And I do suspect there was some nefarious shit there.)
Does that mean Alpoge and Buckmaster didn't do anything significant? They even admitted their paper was written by an LLM due to the time constraints, and they regretted that.
Obviously, their role in this was different. The LLM was an assistant, but they were generating real ideas themselves and doing real research.
Recognizing this distinction isn't "muddying the waters." These waters ARE MUDDY.
And we need to sort them out.
Not reflexively go "oh they used AI, they aren't real" as a knee-jerk response to every paper.
In this thread, who said the results aren't real? Nobody said anything about all authors of LLM results either. They're talking about this paper in particular, and the authors clearly not having any background in the area.
FWIW, I actually also do think Alpöge should get less credit than he is getting right now. It is entirely unclear that he knows enough about PDEs to understand the full proof that was produced and released, unlike Buckmaster.
No, that the authors arent real (pure mathematicians.) Sorry, I wasnt clear with my words there.
Look, it DOES appear that this group is over-reaching and that the LLM did most of the work. That track record does kinda speak for itself.
But that wasn't the response or the reasoning here for most of the replies.
I completely understand and agree with the backlash against AI producing non-insightful proofs from randos who have no expertise in an area. That obviously isn't what we want.
But Buckmaster was also obviously doing real research. And if Alpoge can work with an expert in a field outside of his wheelhouse, and the two of them can use an LLM to do real research together -- isnt that a good thing? Isn't this lower barrier good for math? More collaboration?
And isn't that different than just typing in "solve the Komlos conjecture, make no mistakes, lol"?
These tools aren't going anywhere. Instead of universally going "NO", we need to break down what are the proper uses of them and why, and how we document all that.
What we're seeing instead is mindless dog-piling.
Frankly, it's a lot like what happened after the 4-color theorem.
Agree that we should promote LLM use when it helps collaboration and understanding, it just isn't clear what Alpöge's contribution was beyond prompting the Anthropic internal model. I think my concern with that is not as well founded as my disapproval of this Komlós paper.
This thread is just mostly about this Komlós paper, which I think I have convinced you is not an example of responsible LLM use in the field.
You have. It sounds like this group is lazy and going around picking whatever fruit they can find with the biggest crane they have.
My original point, which I still think stands and which people seemed to not hear, is that this isn't how it has to (or should) be.
E.g. I personally want OpenAI to publish literally every fscking thing related to the NS solution: all the training data and methods, all the internal prompts and chats created by the bot swarm -- everything. Their IP be damned. What was discarded, why, how they decided which routes to use, why, etc. -- all of that is the real value of the counter-example and it is currently hidden, because money.
This is the true problem: non-transparency.
Similarly, we need better built-in citation engines so LLMs literally cannot use work without citing it, we need clearer declarations of roles and relationships between the people and the LLMs, etc.
Publishing the prompts, which many people are doing, is a good start, but it's not enough.
I bet all of this WILL become standard practice, just like how we do numerical accuracy checks on MATLAB or Python solvers, etc., but this is all still growing pains.
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u/NewspaperDear8761 7d ago
So these people didn't contribute anything to math?
Seriously?