Earlier quoted context omitted.
But can it count the R's in strawberry?
That question is equivalent to asking a human to add the wavelengths of those two colors and divide it by 3.
Why is that?
Epoch confirms GPT5.4 Pro solved a frontier math open problem
351–360 of 744 posts
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#352Earlier quoted context omitted.
Writing a complex parser or certainly a compiler is a 1 - 3 month project, for example. Again, I'm not trying to downplay this, but to frame this accurately. I think an AI being able to build a parser/ compiler is cool too. > One of the more strange phenomena with machines getting better and the incessant need (seemingly driven by human exceptionalism) to downplay each result, is that you just end up belittling human…
>Writing a complex parser or certainly a compiler is a 1 - 3 month project, for example. 1. Estimating time completion of something that has been done multiple times before and an open problem that has not yet been solved is a different matter entirely. 1 to 3 months is an educated guess and more likely than not, an underestimate. 2. I do not think months long complex compilers and parsers are being routinely complet…
I don't get what either of your points is intended to demonstrate. Let's revisit the first post I replied to:
> It's deeply surprising to me that LLMs have had more success proving higher math theorems than making successful consumer software
As far as I can tell, they absolutely have not had more success in this area relative to making successful consumer software.
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#353Earlier quoted context omitted.
The difference is whether an entity that can "feel" is in the loop and how much they have contributed to it even if it is a remix.
I think there's demonstrably very little difference at all between human and AI outputs, and that's exactly what freaks people out about it. Else they wouldn't be so obsessed with trying to find and define what makes it different. The Thesis of Everything is a Remix is that there is no difference in how any culture is produced. Different models will have a different flavor to their output in the same way as different…
> I think there's demonstrably very little difference at all between human and AI outputs
Bold claim, as the internet is awash with counterexamples.
In any case, as I think this conversation is trending towards theories of artistic expression, “AI content” will never be truly relatable until it can feel pleasure, pain, and other human urges. The first thing I often think about when I critically assess a piece of art, like music, is what the artist must have been feeling when they created it, and what prompted them to feel that way. I often wonder if AI influencers have ever critically assessed art, or if they actually don’t understand it because of a lack of empathy or something.
And relatability, for me, is the ultimate value of artistic expression.
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#354First prove the solution wasn’t in the training data. Otherwise it’s all just vibes and ‘trust me bro.’
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#355I am kind of amazed at how many commenters respond to this result by confidently asserting that LLMs will never generate 'truly novel' ideas or problem solutions. > AI is a remixer; it remixes all known ideas together. It won't come up with new ideas > it's not because the model is figuring out something new > LLMs will NEVER be able to do that, because it doesn't exist It's not enough to say 'it will never be able t…
I've been working on a utility that lets me "see through" app windows on macOS [1] (I was a dev on Apple's Xcode team and have a strong understanding of how to do this efficiently using private APIs). I wondered how Claude Code would approach the problem. I fully expected it to do something most human engineers would do: brute-force with ScreenCaptureKit. It almost instantly figured out that it didn't have to "see th…
That's actually pretty cool. What made you think of doing this in the first place?
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#356Earlier quoted context omitted.
The ability to learn and infer without absorbing millions of books and all text on internet really does make us special. And only at 20 watts!
Last I checked humans didn't pop into existence doing that. It happened after billions of years of brute force, trial and error evolution. So well done for falling into the exact same trap the OP cautions. Intelligence from scratch requires a mind boggling amount of resources, and humans were no different.
Do you think evolutionary pressures are the best explanation for why humans were able to posit the Poincaré conjecture and solve it? While our mental architecture evolved over a very long time, we still learn from miniscule amounts of data compared to LLMs.
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#357> This problem is about improving lower bounds on the values of a sequence, , that arises in the study of simultaneous convergence of sets of infinite series, defined as follows.
One thing I notice in the AlphaEvolve paper as well as here, is that these LLMs have been shown to solve optimization problems - something we have been using computers for, for really long. In fact, I think the alphaevolve-style prompt augmentation approach is a more principled approach to what these guys have done here, and am fairly confident this one would have been solved in that approach as well.
In spirit, the LLM seems to compute the {meta-, }optimization step()s in activation space. Or, it is retrieving candidate proposals.
It would be interesting to see if we can extract or model the exact algorithms from the activations. Or, it is simply retrieving and proposing a deductive closures of said computation.
In the latter case, it would mean that LLMs alone can never "reason" and you need an external planner+verifier (alpha-evolve style evolutionary planner for example).
We are still looking for proof of the former behaviour.
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#358I don't know why I am still perpetually shocked that the default assumption is that humans are somehow unique. It's this pervasive belief that underlies so much discussion around what it means to be intelligent. The null hypothesis goes out the window. People constantly make comments like "well it's just trying a bunch of stuff until something works" and it seems that they do not pause for a moment to consider whethe…
it is not the assumption that humans are unique. it is that statistical models cannot really think out of the box most of the time
And you know that humans aren't statistical models how?
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#359I am kind of amazed at how many commenters respond to this result by confidently asserting that LLMs will never generate 'truly novel' ideas or problem solutions. > AI is a remixer; it remixes all known ideas together. It won't come up with new ideas > it's not because the model is figuring out something new > LLMs will NEVER be able to do that, because it doesn't exist It's not enough to say 'it will never be able t…
I guess when it can't be tripped up by simple things like multiplying numbers, counting to 100 sequentially or counting letters in a string without writing a python program, then I might believe it. Also no matter how many math problems it solves it still gets lost in a codebase
LLMs are bad at arithmetic and counting by design. It's an intentional tradeoff that makes them better at language and reasoning tasks.
If anybody really wanted a model that could multiply and count letters in words, they could just train one with a tokenizer and training data suited to those tasks. And the model would then be able to count letters, but it would be bad at things like translation and programming - the stuff people actually use LLMs for. So, people train with a tokenizer and training data suited to those tasks, hence LLMs are good at language and bad at arithmetic,
Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem
#360"In this scaffold, several other models were able to solve the problem as well: Opus 4.6 (max), Gemini 3.1 Pro, and GPT-5.4 (xhigh)." I find that very surprising. This problem seems out of reach 3 months ago but now the 3 frontier models are able to solve it. Is everybody distilling each others models? Companies sell the same data and RL environment to all big labs? Anybody more involved can share some rumors? :P I d…
Is it possible the AI labs are seeding their models with these solved problems? Like, if I was Sam Altman with a bazillion dollars of investment I would pay some mathematicians to solve some of these problems so that the models could "solve" them later on. Not that I think it's what's happening here of course...
But it is pretty funny how 5.4 miscounted the number of 1's in 18475838184729 on the same day it solved this.