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Epoch confirms GPT5.4 Pro solved a frontier math open problem

epoch.ai

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Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#621

Post by zeroonetwothree »

gpderetta wrote: post #438
brenschluss wrote: post #149

Earlier quoted context omitted.

sigh; this argument is the new Chinese Room; easily described, utterly wrong. https://www.youtube.com/watch?v=YEUclZdj_Sc

After dismissing it for a long time, I have come around to the philosophical zombie argument. I do not believe that LLMs are conscious, but I also no longer believe that consciousness is a prerequisite for intelligence. I think at this point it is hard to deny that LLMs do not possess some form of intelligence (although not necessarily human-like). I think P-zombies is a fitting description.

I don't think P-zombies can exist. There must be some perceptible difference between an intelligence w/ consciousness and one without. The only way there wouldn't be a difference is if we are mistaken about the consciousness (either both have it or neither do).

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#622

Post by kenmu »

Is their scaffold available? Does it do anything special beyond feeding the warmup, single challenge, and full problem to an LLM? Because it's interesting that GPT-5.2 Pro, arguably the best model until a few months ago, couldn't even solve the warmup. And now every frontier model can solve the full problem. Even the non-Pro GPT-5.4. Also strange that Gemini 3 Deep Think couldn't solve it, whereas Gemini 3.1 Pro could. I read that Deep Think is based on 3.1 Pro. Is that correct?

I see that GPT-5.2 Pro and Gemini 3 Deep Think simply had the problems entered into the prompt. Whereas the rest of the models had a decent amount of context, tips, and ideas prefaced to the problem. Were the newer models not able to solve this problem without that help?

Anyway, impressive result regardless of whether previous models could've also solved it and whether the extra context was necessary.

I know these frontier models behave differently from each other. I wonder how many problems they could solve combining efforts.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#623

Post by qnleigh »

coldtea wrote: post #618
qnleigh wrote: post #595

Earlier quoted context omitted.

> It doesn't discern between them, just looks for the best statistical fit Of course at the lowest level, LLMs are trained on next-token prediction, and on the surface, that looks like a statistics problem. But this is an incredibly reductionist viewpoint and I don't see how it makes any empirically testable predictions about their limits. LLMs 'learned' a lot of math and science in this way. > "truly novel" and "sol…

> LLMs 'learned' a lot of math and science in this way. Did they? Or is it begging the question?

This is why I put 'learned' in quotes. They started from a state of not being able to solve algebra problems or produce basic steps of scientific reasoning to being able to. Operationally, that is what I mean by learning and they unambiguously do it.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#624

Post by rfw300 »

kuschku wrote: post #243
chromacity wrote: post #96

Earlier quoted context omitted.

You're kidding, but it could be true? Many areas of mathematics are, first and foremost, incredibly esoteric and inaccessible (even to other mathematicians). For this one, the author stated that there might be 5-10 people who have ever made any effort to solve it. Further, the author believed it's a solvable problem if you're qualified and grind for a bit. In software engineering, if only 5-10 people in the world hav…

If only 5-10 people have ever tried to solve something in programming, every LLM will start regurgitating your own decade-old attempt again and again, sometimes even with the exact comments you wrote back then (good to know it trained on my GitHub repos...), but you can spend upwards of 100mio tokens in gemini-cli or claude code and still not make any progress. It's afterall still a remix machine, it can only interpo…

What is a "truly new task"? Does there exist such a thing? What's an example of one?

Everything we do builds on top of what's already been done. When I write a new program, I'm composing a bunch of heuristics and tricks I've learned from previous programs. When a mathematician approaches an open problem, they use the tactics they've developed from their experience. When Newton derived the laws of physics, he stood on the shoulders of giants. Sure, some approaches are more or less novel, but it's a difference in degree, not kind. There's no magical firebreak to separate what AI is doing or will do, and the things the most talented humans do.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#625

Post by zeroonetwothree »

anthonyrstevens wrote: post #571
trapatsas wrote: post #436

I feel like this single image perfectly sums up the entire thread here: https://trapatsas.eu/sites/llm-predictions/

Yes, and no matter when "now" is, the doubters will always see in their mind's eye the flat line extending to the right.

That's tautological

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#626

Post by zeroonetwothree »

trapatsas wrote: post #436

I feel like this single image perfectly sums up the entire thread here: https://trapatsas.eu/sites/llm-predictions/

It's not like this is new to AI

https://oertx.highered.texas.gov/courseware/lesson/1849/over...

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#627

Post by zeroonetwothree »

bandrami wrote: post #135

It's deeply surprising to me that LLMs have had more success proving higher math theorems than making successful consumer software

Pretty much all consumer software made in 2026 is heavily using AI in its development. So I'm not sure what basis you have for your assertion.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#628

Post by kuschku »

rfw300 wrote: post #624
kuschku wrote: post #243

Earlier quoted context omitted.

If only 5-10 people have ever tried to solve something in programming, every LLM will start regurgitating your own decade-old attempt again and again, sometimes even with the exact comments you wrote back then (good to know it trained on my GitHub repos...), but you can spend upwards of 100mio tokens in gemini-cli or claude code and still not make any progress. It's afterall still a remix machine, it can only interpo…

What is a "truly new task"? Does there exist such a thing? What's an example of one? Everything we do builds on top of what's already been done. When I write a new program, I'm composing a bunch of heuristics and tricks I've learned from previous programs. When a mathematician approaches an open problem, they use the tactics they've developed from their experience. When Newton derived the laws of physics, he stood on…

That highlighted phrase "everything is a remix" was for a good reason, there's a documentary of that same name, and I can certainly recommend it.

At the same time, there are things that are truly novel, even if the idea is based on combining two common approaches, the implementation might need to be truly novel, with new formulas and new questions that arise from those. AI can't belp there, speaking from experience.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#629

Post by oofbey »

qnleigh wrote: post #597
qnleigh wrote: post #288

I 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 might as well answer my own question, because I do think there are some coherent arguments for fundamental LLM limitations: 1. LLMs are trained on human-quality data, so they will naturally learn to mimic our limitations. Their capabilities should saturate at human or maybe above-average human performance. 2. LLMs do not learn from experience. They might perform as well as most humans on certain tasks, but a human…

The idea that they don’t learn from experience might be true in some limited sense, but ignores the reality of how LLMs are used. If you look at any advanced agentic coding system the instructions say to write down intermediate findings in files and refer to them. The LLM doesn’t have to learn. The harness around it allows it to. It’s like complaining that an internal combustion engine doesn’t have wheels to push it around.
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