Yes and 5.3 and the latest codex cli client is incredibly good across compactions. Anyone know the methodology they're using to maintain state and manage context for a 12 hour run? It could be as simple as a single dense document and its own internal compaction algrorithm, I guess.

after those 30 min you can manually ask it again to continue working on the problem

In my experience humans can make new things when they are some linear combination of existing things but I haven’t been able to get them to do something totally out of distribution yet from first principles[0].

[0]: https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...

Anything but recent.

https://developers.openai.com/cookbook/articles/codex_exec_p... might be what you're looking for

Well they (OpenAI) never made such a claim. And yes, LLMs have made unique solutions/contributions to a few erdos problems.

Well it’ll be at least a whole month before some other company announces similar capability. The moat will hold!

> found a formula that humans couldn't

Couldn't is an immensely high bar in this context, didn't seems more appropriate and renders this whole thing slightly less exciting.

It's easy to fall into a negative mindset when there are legions of pointy haired bosses and bandwagoning CEOs who (wrongly) point at breakthroughs like this as justification for AI mandates or layoffs.

It's an obvious tension created by the title.

The reality is: "GPT 5.2 found a more general and scalable form of an equation, after crunching for 12 hours supervised by 4 experts in the field".

Which is equivalent to taking some of the countless niche algorithms out there and have few experts in that algo have LLMs crunch tirelessly till they find a better formula. After same experts prompted it in the right direction and with the right feedback.

Interesting? Sure. Speaks highly of AI? Yes.

Does it suggest that AI is revolutionizing theoretical physics on its own like the title does? Nope.

It is not only the the peanut gallery that is skeptical:

https://www.math.columbia.edu/~woit/wordpress/?p=15362

Let's wait a couple of days whether there has been a similar result in the literature.

Let’s have some compassion, a lot of people are freaking out about their careers now and defense mechanisms are kicking in. It’s hard for a lot of people to say “actually yeah this thing can do most of my work now, and barrier of entry dropped to the ground”.

>AI can be an amazing productivity multiplier for people who know what they're doing.

>[...]

>The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding.

You're sort of acting like it's all or nothing. What about the the humans that used to be that "force multiplier" on a team with the person guiding the research?

If a piece of software required a team of ten to people, and instead it's built with one engineer overseeing an AI, that's still 90% job loss.

For a more current example: do you think all the displaced Uber/Lyft drivers aren't going to think "AI took my job" just because there's a team of people in a building somewhere handling the occasional Waymo low confidence intervention, as opposed to being 100% autonomous?

I'm not sure you can call something an optimizing C compiler if it doesn't optimize or enforce C semantics (well, it compiles C but also a lot of things that aren't syntactically valid C). It seemed to generate a lot of code (wow!) that wasn't well-integrated and didn't do what it promised to, and the human didn't have the requisite expertise to understand that. I'm not a theoretical physicist but I will hold to my skepticism here, for similar reasons.

> The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding.

It's also a legitimate concern. We happen to be in a place where humans are needed for that "last critical 10%," or the first critical 10% of problem formulation, and so humans are still crucial to the overall system, at least for most complex tasks.

But there's no logical reason that needs to be the case. Once it's not, humans will be replaced.

Actually, the results were far worse and way less impressive than what the media said.

Right. If it hadn't been Nicholas Carlini driving Claude, with his decades of experience, there wouldn't be a Claude c compiler. It still required his expertise and knowledge for it to get there.

Wasnt that like some marketing bro? This is coming out the front door with serious physicists attached.

> In my experience LLM’s can make new things when they are some linear combination of existing things but I haven’t been to get them to do something totally out of distribution yet from first principles.

What's the distinction between "first principles" and "existing things"?

I'm sympathetic to the idea that LLMs can't produce path-breaking results, but I think that's true only for a strict definition of path-breaking (that is quite rare for humnans too).

Hmm feels a bit trivializing, we don't know exactly how difficult it was to come up with the generic set of equations mentioned from the human starting point.

I can claim some knowledge of physics from my degree, typically the easy part is coming up with complex dirty equations that work under special conditions, the hard part is the simplification into something elegant, 'natural' and general.

Also "LLM’s can make new things when they are some linear combination of existing things"

Doesn't really mean much, what is a linear combination of things you first have to define precisely what a thing is?

> for people who know what they're doing.

I worry we're not producing as many of those as we used to

How was that not the case? As far as I understand it ChatGPT was instrumental to solving a problem. Even if it did not entirely solve it by itself, the combination with other tools such as Lean is still very impressive, no?

This is the critical bit (paraphrasing):

Humans have worked out the amplitudes for integer n up to n = 6 by hand, obtaining very complicated expressions, which correspond to a “Feynman diagram expansion” whose complexity grows superexponentially in n. But no one has been able to greatly reduce the complexity of these expressions, providing much simpler forms. And from these base cases, no one was then able to spot a pattern and posit a formula valid for all n. GPT did that.

Basically, they used GPT to refactor a formula and then generalize it for all n. Then verified it themselves.

I think this was all already figured out in 1986 though: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.56... see also https://en.wikipedia.org/wiki/MHV_amplitudes

> but I haven’t been to get them to do something totally out of distribution yet from first principles

Can humans actually do that? Sometimes it appears as if we have made a completely new discovery. However, if you look more closely, you will find that many events and developments led up to this breakthrough, and that it is actually an improvement on something that already existed. We are always building on the shoulders of giants.

"GPT did this". Authored by Guevara (Institute for Advanced Study), Lupsasca (Vanderbilt University), Skinner (University of Cambridge), and Strominger (Harvard University).

Probably not something that the average GI Joe would be able to prompt their way to...

I am skeptical until they show the chat log leading up to the conjecture and proof.

When chess engines were first developed, they were strictly worse than the best humans. After many years of development, they became helpful to even the best humans even though they were still beatable (1985–1997). Eventually they caught up and surpassed humans but the combination of human and computer was better than either alone (~1997–2007). Since then, humans have been more or less obsoleted in the game of chess.

Five years ago we were at Stage 1 with LLMs with regard to knowledge work. A few years later we hit Stage 2. We are currently somewhere between Stage 2 and Stage 3 for an extremely high percentage of knowledge work. Stage 4 will come, and I would wager it's sooner rather than later.

I believe Gemini holds the moat now.

I'd say "couldn't in 20 hours" might be more defensible. Depends on how many humans though. "couldn't in 20 GPT watt-hours" would give us like 2,000 humans or so.

Yes, all of these stories, and frequent model releases are just intended to psyop "decision makers" into validating their longstanding belief that the labour shouldn't be as big of a line item in a companies expenses, and perhaps can be removed altogether.. They can finally go back to the good old days of having slaves (in the form of "agentic" bots), they yearn to own slaves again.

CEOs/decision makers would rather give all their labour budget to tokens if they could just to validate this belief. They are bitter that anyone from a lower class could hold any bargaining chips, and thus any influence over them. It has nothing to do with saving money, they would gladly pay the exact same engineering budget to Anthropic for tokens (just like the ruling class in times past would gladly pay for slaves) if it can patch that bitterness they have for the working class's influence over them.

The inference companies (who are also from this same class of people) know this, and are exploiting this desire. They know if they create the idea that AI progress is at an unstoppable velocity decision makers will begin handing them their engineering budgets. These things don't even have to work well, they just need to be perceived as effective, or soon to be for decision makers to start laying people off.

I suspect this is going to backfire on them in one of two ways.

1. French Revolution V2, they all get their heads cutoff in 15 years, or an early retirement on a concrete floor.

2. Many decisions makers will make fools of themselves, destroy their businesses and come begging to the working class for our labor, giving the working class more bargaining chips in the process.

Either outcome is going to be painful for everyone, lets hope people wake up before we push this dumb experiment too far.

Well those Uber drivers are usually pretty quick to note that Uber is not their job, just a side hustle. It's too bad I won't know what they think by then since we won't be interacting any more.

I am constantly seeing this thing do most of my work (which is good actually, I don't enjoy typing code), but requiring my constant supervision and frequent intervention and always trying to sneak in subtle bugs or weird architectural decisions that, I feel with every bone in my body, would bite me in the ass later. I see JS developers with little experience and zero CS or SWE education rave about how LLMs are so much better than us in every way, when the hardest thing they've ever written was bubble sort. I'm not even freaking about my career, I'm freaking about how much today's "almost good" LLMs can empower incompetence and how much damage that could cause to systems that I either use or work on.

Yeah but you know what, this is a complete psyop.

They just want people to think the barrier of entry has dropped to the ground and that value of labour is getting squashed, so society writes a permission slip for them to completely depress wages and remove bargaining chips from the working class.

Don't fall for this, they want to destroy any labor that deals with computer I/0, not just SWE. This is the only value "agentic tooling" provides to society, slaves for the ruling class. They yearn for the opportunity to own slaves again.

It can't do most of your work, and you know that if you work on anything serious. But If C-suite who hasn't dealt with code in two decades, thinks this is the case because everyone is running around saying its true they're going to make sure they replace humans with these bot slaves, they really do just want slaves, they have no intention of innovating with these slaves. People need to work to eat, now unless LLMs are creating new types of machines that need new types of jobs, like previous forms of automation, then I don't see why they should be replacing the human input.

If these things are so good for business, and are pushing software development velocity.. Why is everything falling apart? Why does the bulk of low stakes software suck. Why is Windows 11 so bad? Why aren't top hedge funds, medical device manufactures (places where software quality is high stakes) replacing all their labor? Where are the new industries? They don't do anything novel, they only serve to replace inputs previously supplied by humans so the ruling class can finally get back to good old feeling of having slaves that can't complain.

The reason there is a marketing opportunity is because, to your point, there is a legitimate concern. Marketing builds and amplifies the concern to create awareness.

When the systems turn into something trivial to manage with the new tooling, humans build more complex or add more layers on the existing systems.

> GPT 5.2 after crunching 12 hours mathematical formulas supervised and prompted by 4 experts in the field

Yet, if some student or child achieved the same – under equal supervision – we would call him the next Einstein.

For the sake of clarity: Woit's post is not about the same alleged instance of GPT producing new work in theoretical physics, but about an earlier one from November 2025. Different author, different area of theoretical physics.

sure, I won't argue on this, although it did manage to deliver the marketing value they were looking for, at the end their goal was not to replace gcc but to make people talk about AI and Anthropic.

What I said in my original comment is that AI delivers when it's used by experts, in this case there was someone who was definitely not a C compiler expert, what would happen if there was a real expert doing this?

the c compiler results or the physics results this post is about?

What does a 12-hour solution cost an OpenAI customer?

Insert perfunctory HN reply of "but do humans ever do anything totally out of distribution from first principles?"

(This is deep)

Depends on what you think is valid.

The process you’re describing is humans extending our collective distribution through a series of smaller steps. That’s what the “shoulders of giants” means. The result is we are able to do things further and further outside the initial distribution.

So it depends on if you’re comparing individual steps or just the starting/ending distributions.

That paper from the 80s (which is cited in the new one) is about "MHV amplitudes" with two negative-helicity gluons, so "double-minus amplitudes". The main significance of this new paper is to point out that "single-minus amplitudes" which had previously been thought to vanish are actually nontrivial. Moreover, GPT-5.2 Pro computed a simple formula for the single-minus amplitudes that is the analogue of the Parke-Taylor formula for the double-minus "MHV" amplitudes.

It bears repeating that modern LLMs are incredibly capable, and relentless, at solving problems that have a verification test suite. It seems like this problem did (at least for some finite subset of n)!

This result, by itself, does not generalize to open-ended problems, though, whether in business or in research in general. Discovering the specification to build is often the majority of the battle. LLMs aren't bad at this, per se, but they're nowhere near as reliably groundbreaking as they are on verifiable problems.

I'm not sure if GPTs ability goes beyond a formal math package's in this regard or its just its just way more convienient to ask ChatGPT rather than using these software.

> Can humans actually do that?

From my reading yes, but I think I am likely reading the statement differently than you are.

> from first principles

Doing things from first principles is a known strategy, so is guess and check, brute force search, and so on.

For an llm to follow a first principles strategy I would expect it to take in a body of research, come up with some first principles or guess at them, then iteratively construct and tower of reasonings/findings/experiments.

Constructing a solid tower is where things are currently improving for existing models in my mind, but when I try openai or anthropic chat interface neither do a good job for long, not independently at least.

Humans also often have a hard time with this in general it is not a skill that everyone has and I think you can be a successful scientist without ever heavily developing first principles problem solving.

It's a bit unclear to me what happens if I do that after it thinks for 30 minutes and ends with no response. Does it start off where it left off? Does it start from scratch again? Like I don't know how the compaction of their prior thinking traces work

We will be producing them even less. I fear for the future graduates, hell even for school children, who are now uncontrollably using ChatGPT for their homework. Next level brainrot

  > Can humans actually do that? 

Yes

Seriously, think about it for a second...

If that were true then science should have accelerated a lot faster. Science would have happened differently and researchers would have optimized to trying to ingest as many papers as they can.

Dig deep into things and you'll find that there are often leaps of faith that need to be made. Guesses, hunches, and outright conjectures. Remember, there are paradigm shifts that happen. There are plenty of things in physics (including classical) that cannot be determined from observation alone. Or more accurately, cannot be differentiated from alternative hypotheses through observation alone.

I think the problem is when teaching science we generally teach it very linearly. As if things easily follow. But in reality there is generally constant iterative improvements but they more look like a plateau, then there are these leaps. They happen for a variety of reasons but no paradigm shift would be contentious if it was obvious and clearly in distribution. It would always be met with the same response that typical iterative improvements are met with "well that's obvious, is this even novel enough to be published? Everybody already knew this" (hell, look at the response to the top comment and my reply... that's classic "Reviewer #2" behavior). If it was always in distribution progress would be nearly frictionless. Again, with history in how we teach science we make an error in teaching things like Galileo, as if The Church was the only opposition. There were many scientists that objected, and on reasonable grounds. It is also a problem we continually make in how we view the world. If you're sticking with "it works" you'll end up with a geocentric model rather than a heliocentric model. It is true that the geocentric model had limits but so did the original heliocentric model and that's the reason it took time to be adopted.

By viewing things at too high of a level we often fool ourselves. While I'm criticizing how we teach I'll also admit it is a tough thing to balance. It is difficult to get nuanced and in teaching we must be time effective and cover a lot of material. But I think it is important to teach the history of science so that people better understand how it actually evolves and how discoveries were actually made. Without that it is hard to learn how to actually do those things yourself, and this is a frequent problem faced by many who enter PhD programs (and beyond).

  > We are always building on the shoulders of giants.

And it still is. You can still lean on others while presenting things that are highly novel. These are not in disagreement.

It's probably worth reading The Unreasonable Effectiveness of Mathematics in the Natural Sciences. It might seem obvious now but read carefully. If you truly think it is obvious that you can sit in a room armed with only pen and paper and make accurate predictions about the world, you have fooled yourself. You have not questioned why this is true. You have not questioned when this actually became true. You have not questioned how this could be true.

https://www.hep.upenn.edu/~johnda/Papers/wignerUnreasonableE...

  You are greater than the sum of your parts

Still pretty awesome though, if you ask me.

Relativity comes to mind.

You could nitpick a rebuttal, but no matter how many people you give credit, general relativity was a completely novel idea when it was proposed. I'd argue for special relatively as well.

It didn't solve it, it simply found that it had been solved in a publication and that the list of open problems wasn't updated.

You should probably email the authors if you think that's true. I highly doubt they didn't do a literature search first though...

Go enough shoulders down, and someone had to have been the first giant.

Serious questions, I often hear about this "let the LLM cook for hours" but how do you do that in practice and how does it manages its own context? How doesn't it get lost at all after so many tokens?

  > I think this was all already figured out in 1986 though

They cite that paper in the third paragraph...

  Naively, the n-gluon scattering amplitude involves order n! terms. Famously, for the special case of MHV (maximally helicity violating) tree amplitudes, Parke and Taylor [11] gave a simple and beautiful, closed-form, single-term expression for all n.

It also seems to be a main talking point.

I think this is a prime example of where it is easy to think something is solved when looking at things from a high level but making an erroneous conclusion due to lack of domain expertise. Classic "Reviewer 2" move. Though I'm not a domain expert and so if there was no novelty over Parke and Taylor I'm pretty sure this will get thrashed in review.

We would not call him at all because it would be one of the many millions that went through projects like this for their thesis as physics or math graduates.

One of my best friends in his bachelor thesis had solved a difficult mathematical problem in planet orbits or something, and it was just yet another random day in academia.

And she didn't solve it because she was a genius but because there's a bazillions such problems out there and little time to look at them and focus. Science is huge.

This thread is about "whenever a new breakthrough in AI use comes up", and the comment you reply to correctly points out skepticism for the general case and does not claim any relation to the current case.

You reached your goal though and got that comment downvoted.

Deliver what exactly? False hope and lies?

https://github.com/anthropics/claudes-c-compiler/issues/228

The C compiler.

Even if I grant you that, surely we’ve moved the goal posts a bit if we’re saying the only thing we can think of that AI can’t do is the life’s work of a man who’s last name is literally synonymous with genius.

I think even “non-intelligent” solver like Mathematica is cool - so hell yes, this is cool.

Big difference between “derives new result” and “reproduces something likely in its training dataset”.

They also reference Parke and Taylor. Several times...

Probably not homo sapiens.. other hominids older than us developed a lot of technology

You should be more skeptical of marketing releases like this. This is an advertisement.

From what I've seen is a process of compacting the session once it reaches some limit, which basically means summarizing all the previous work and feeding it as the initial prompt for the next session.

"Grad Student did this". Co-authored by <Famous advisor 1>, <Famous advisor 2>, <Famous advisor 3>.

Is this so different?

I’m guessing, would love someone who has first hand knowledge to comment. But my guess is it’s some combination of trying many different approaches in parallel (each in a fresh context), then picking the one that works, and splitting up the task into sequential steps, where the output of one step is condensed and is used as an input to the next step (with possibly human steering between steps)

His point is going to be some copium like since the c compiler is not as optimized as gcc, it was not impressive.

My understanding is there's been around 10 erdos problems solved by GPT by now. Most of them have been found to be either in literature or a very similar problem was solved in literature. But one or two solutions are quite novel.

https://github.com/teorth/erdosproblems/wiki/AI-contribution... may be useful

I am not a scientific historian, or even a physicist, but IMO relativity has a weak case for being a completely novel discovery. Critique of absolute time and space of Newtonian physics was already well underway, and much of the methodology for exploring this relativity (by way of gyroscopes, inertial reference frames, and synchronized mechanical clocks) were already in parlance. Many of the phenomena that relativity would later explain under a consistent framework already had independent quasi-explanations hinting at the more universal theory. Poincare probably came the closest to unifying everything before Einstein:

> In 1902, Henri Poincaré published a collection of essays titled Science and Hypothesis, which included: detailed philosophical discussions on the relativity of space and time; the conventionality of distant simultaneity; the conjecture that a violation of the relativity principle can never be detected; the possible non-existence of the aether, together with some arguments supporting the aether; and many remarks on non-Euclidean vs. Euclidean geometry.

https://en.wikipedia.org/wiki/History_of_special_relativity

Now, if I had to pick a major idea that seemed to drop fully-formed from the mind of a genius with little precedent to have guided him, I might personally point to Galois theory (https://en.wikipedia.org/wiki/Galois_theory). (Ironically, though, I'm not as familiar with the mathematical history of that time and I may be totally wrong!)

That's not exactly true. Lorentz contraction is a clear antecedent to special relativity.

Not really. Pretty sure I read recently that Newton appreciated that his theory was non-local and didn't like what Einstein later called "spooky action at a distance". The Lorentz transform was also known from 1887. Time dilation was understood from 1900. Poincaré figured out in 1905 that it was a mathematical group. Einstein put a bow on it all by figuring out that you could derive it from the principle of relativity and keeping the speed of light constant in all inertial reference frames.

I'm not sure about GR, but I know that it is built on the foundations of differential geometry, which Einstein definitely didn't invent (I think that's the source of his "I assure you whatever your difficulties in mathematics are, that mine are much greater" quote because he was struggling to understand Hilbert's math).

And really Cauchy, Hilbert, and those kinds of mathematicians I'd put above Einstein in building entirely new worlds of mathematics...

The paper has all those prominent institutions who acknowledge the contribution so realistically, why would you be skeptical ?

I'm a big LLM sceptic but that's… moving the goalposts a little too far. How could an average Joe even understand the conjecture enough to write the initial prompt? Or do you mean that experts would give him the prompt to copy-paste, and hope that the proverbial monkey can come up with a Henry V? At the very least posit someone like a grad student in particle physics as the human user.

Don't underestimate the willingness of physicists to skimp on literature review.

With a chess engine, you could ask any practitioner in the 90's what it would take to achieve "Stage 4" and they could estimate it quite accurately as a function of FLOPs and memory bandwidth. It's worth keeping in mind just how little we understand about LLM capability scaling. Ask 10 different AI researchers when we will get to Stage 4 for something like programming and you'll get wild guesses or an honest "we don't know".

Pythagoras is the turtle.

So it's a garbage headline, from an AI vendor, trying to increase hype and froth around what they are selling, when in fact the "new result" has been a solved problem for almost 40 years? Am I getting that right?

You're right. Parke & Taylor showed the simplest nonzero amplitudes have two minus helicities while one-minus amplitudes vanish (generically). This paper claims that vanishing theorem has a loophole - a new hidden sector exists and one-minus amplitudes are secretly there, but distributional

The evolution was also interesting: first the engines were amazing tactically but pretty bad strategically so humans could guide them. With new NN based engines they were amazing strategically but they sucked tactically (first versions of Leela Chess Zero). Today they closed the gap and are amazing at both strategy and tactics and there is nothing humans can contribute anymore - all that is left is to just watch and learn.

We are already at stage 3 for software development and arguably step 4

You probably don’t know what you’re talking about.

I am not aware of any unsolved Erdos problem that was solved via an LLM. I am aware of LLMs contributing to variations on known proofs of previously solved Erdos problems. But the issue with having an LLM combine existing solutions or modify existing published solutions is that the previous solutions are in the training data of the LLM, and in general there are many options to make variations on known proofs. Most proofs go through many iterations and simplifications over time, most of which are not sufficiently novel to even warrant publication. The proof you read in a textbook is likely a highly revised and simplified proof of what was first published.

If I'm wrong, please let me know which previously unsolved problem was solved, I would be genuinely curious to see an example of that.

Some of these were initially hyped as novel solutions, and then were quietly downgraded after it was discovered the solutions weren’t actually novel.

...am I wrong in thinking that 1(a) is the relevant section here, and shows a lot of red?

Agree with you everywhere. Although I prefer the quote:

"Since the mathematicians have invaded the theory of relativity, I do not understand it myself anymore."

:)

From that article:

> The quintic was almost proven to have no general solutions by radicals by Paolo Ruffini in 1799, whose key insight was to use permutation groups, not just a single permutation.

Thing is, I am usually the kind of person who defends the idea of a lone genius. But I also believe there is a continuous spectrum, no gaps, from the village idiot to Einstein and beyond.

Let me introduce, just for fun, not for the sake of any argument, another idea from math which I think it came really out of the blue, to the degree that it's still considered an open problem to write an exposition about it, since you cannot smoothly link it to anything else: forcing.

  > Critique of absolute time and space of Newtonian physics was already well underway

This only means Einstein was not alone, it does not mean the results were in distribution.

  > Many of the phenomena that relativity would later explain under a consistent framework already had independent quasi-explanations hinting at the more universal theory.

And this comes about because people are looking at edge cases and trying to solve things. Sometimes people come up with wild and crazy solutions. Sometimes those solutions look obvious after they're known (though not prior to being known, otherwise it would have already been known...) and others don't.

Your argument really makes the claim that since there are others pursuing similar directions that this means it is in distribution. I'll use a classic statistics style framing. Suppose we have a bag with n red balls and p blue balls. Someone walks over and says "look, I have a green ball" and someone else walks over and says "I have a purple one" and someone else comes over and says "I have a pink one!". None of those balls were from the bag we have. There are still n+p balls in our bag, they are still all red or blue despite there being n+p+3 balls that we know of.

  > I am not a [...] physicist

I think this is probably why you don't have the resolution to see the distinctions. Without a formal study of physics it is really hard to differentiate these kinds of propositions. It can be very hard even with that education. So be careful to not overly abstract and simplify concepts. It'll only deprive you of a lot of beauty and innovation.

Right on with special relativity—Lorentz also was developing the theory and was a bit sour that Einstein got so much credit. Einstein basically said “what if special relativity were true for all of physics”, not just electromagnetism, and out dropped e=mc^2. It was a bold step but not unexplainable.

As for general relativity, he spent several years working to learn differential geometry (which was well developed mathematics at the time, but looked like abstract nonsense to most physicists). I’m not sure how he was turned on to this theory being applicable to gravity, but my guess is that it was motivated by some symmetry ideas. (It always come down to symmetry.)

After last month’s Erdos problems handling by LLMs at this point everyone writing papers should be aware that literature checks are approximately free, even physicists.

And their predictions about Go were wrong, because they thought the algorithm would forever be α-β pruning with a weak value heuristic

That is not what happened with chess engines. We didn’t just throw better hardware at it, we found new algorithms, improved the accuracy and performance of our position evaluation functions, discovered more efficient data structures, etc.

People have been downplaying LLMs since the first buzzword garbage scientific paper made its way past peer review and into publication. And yet they keep getting better and better to the point where people are quite literally building projects with shockingly little human supervision.

By all means, keep betting against them.

> With a chess engine, you could ask any practitioner in the 90's what it would take to achieve "Stage 4" and they could estimate it quite accurately as a function of FLOPs and memory bandwidth.

And the same practitioners said right after deep blue that go is NEVER gonna happen. Too large. The search space is just not computable. We'll never do it. And yeeeet...

Chess grandmasters are living proof that it’s possible to reach grandmaster level in chess on 20W of compute. We’ve got orders of magnitude of optimizations to discover in LLMs and/or future architectures, both software and hardware and with the amount of progress we’ve got basically every month those ten people will answer ‘we don’t know, but it won’t be too long’. Of course they may be wrong, but the trend line is clear; Moore’s law faced similar issues and they were successively overcome for half a century.

IOW respect the trend line.

No

Pythagoras learned from Egyptians that have been largely erased by euro/western narratives of superiority.

they probably also acknowledge pytorch, numpy, R ... but we don't attribute those tools as the agent who did the work.

I know we've been primed by sci-fi movies and comic books, but like pytorch, gpt-5.2 is just a piece of software running on a computer instrumented by humans.

Their point is, would you be able to prompt your way to this result? No. Already trained physicists working at world-leading institutions could. So what progress have we really made here?

I would interpret it as implying that the result was due to a lot more hand-holding that what is let on.

Was the initial conjecture based on leading info from the other authors or was it simply the authors presenting all information and asking for a conjecture?

Did the authors know that there was a simpler means of expressing the conjecture and lead GPT to its conclusion, or did it spontaneously do so on its own after seeing the hand-written expressions.

These aren't my personal views, but there is some handwaving about the process in such a way that reads as if this was all spontaneous involvement on GPTs end.

But regardless, a result is a result so I'm content with it.

That's kinda the whole point.

SpaceX can use an optimization algorithm to hoverslam a rocket booster, but the optimization algorithm didn't really figure it out on its own.

The optimization algorithm was used by human experts to solve the problem.

hey, GPT, solve this tough conjecture I've read about on Quanta. make no mistakes

you’re not, and you might have a slight reading comprehension problem

Why wasn't the C compiler it made impressive to you?

> simplest nonzero amplitudes have two minus helicities while one-minus amplitudes vanish

Sorry but I just have to point out how this field of maths read like Star Trek technobabble too me.

It's in the link above, but you can look at #1051 or #851 on the erdosproblems website.

Yeah that was also my take-away when I was following the developments on it. But then again I don't follow it very closely so _maybe_ some novel solutions are discovered. But given how LLMs work, I'm skeptical about that.

I honestly don't see the point of the red data points. By now all the erdos problems have been attempted by AIs--so every unsolved one can be a red data point.

I don't see the authors of those libraries getting a credit on the paper, do you ?

>I know we've been primed by sci-fi movies and comic books, but like pytorch, gpt-5.2 is just a piece of software running on a computer instrumented by humans.

Sure

And we are just a system running on carbon-based biology in our physics computer run by whomever. What makes us special, to say that we are different than GPT-5.2?

It's a stupid point then. Are you able to work with a world leading physicist to any significant degree? No

In this case there certainly were experts doing hand-holding. But simply being able to ask the right question isn't too much to ask, is it? If it had been merely a grad student or even a PhD student who had asked ChatGPT to figure out the result, and ChatGPT had done that, even interactively with the student, this would be huge news. But an average person? Expecting LLMs to transcend the GIGO principle is a bit too much.

Surely you can explain it yourself, then? A person such as yourself with normal reading comprehension would be able to analyze and synthesize this on your own, without asking an LLM for help, no?

Hi I am an author of the paper. We believed that a simple formula should exist but had not been able to find it despite significant effort. It was a collaborative effort but GPT definitely solved the problem for us.

Where do you think Star Trek got its technobabble from?

The erdosproblems website shows 851 was proved in 1934. https://www.erdosproblems.com/851

I guess 1051 qualifies - from the paper: "Semi-autonomous mathematical discovery with gemini" https://arxiv.org/pdf/2601.22401

"We tentatively believe Aletheia’s solution to Erdős-1051 represents an early example of an AI system autonomously resolving a slightly non-trivial open Erdős problem of somewhat broader (mild) mathematical interest, for which there exists past literature on closely-related problems [KN16], but none fully resolves Erdős-1051. Moreover, it does not appear to us that Aletheia’s solution is directly inspired by any previous human argument (unlike in many previously discussed cases), but it does appear to involve a classical idea of moving to the series tail and applying Mahler’s criterion. The solution to Erdős-1051 was generalized further, in a collaborative effort by Aletheia together with human mathematicians and Gemini Deep Think, to produce the research paper [BKK+26]."

> And we are just a system running on carbon-based biology in our physics computer run by whomever. What makes us special, to say that we are different than GPT-5.2?

Do you really want to be treated like an old PC (dismembered, stripped for parts, and discarded) when your boss is done with you (i.e. not treated specially compared to a computer system)?

But I think if you want a fuller answer, you've got a lot of reading to do. It's not like you're the first person in the world to ask that question.

It's always a value decision. You can say shiny rocks are more important than people and worth murdering over.

Not an uncommon belief.

Here you are saying you personally value a computer program more than people

It exposes a value that you personally hold and that's it

That is separate from the material reality that all this AI stuff is ultimately just computer software... It's an epistemological tautology in the same way that say, a plane, car and refrigerator are all just machines - they can break, need maintenance, take expertise, can be dangerous...

LLMs haven't broken the categorical constraints - you've just been primed to think such a thing is supposed to be different through movies and entertainment.

I hate to tell you but most movie AIs are just allegories for institutional power. They're narrative devices about how callous and indifferent power structures are to our underlying shared humanity

It's like saying: calculator drives new result in theoretical physics

(In the hands of leading experts.)

Do you also work at OpenAI? A comment pointing that out was flagged by the LLM marketers.

Oh that's really cool, I am not versed in physics by any means, can you explain how you believed there to be a simple formula but were unable to find it? What would lead you to believe that instead of just accepting it at face value?

Have I got a skill for you!

trekify/SKILL.md: https://github.com/SimHacker/moollm/blob/main/skills/trekify...

"The erdosproblems website shows 851 was proved in 1934." I disagree with this characterization of the Erdos problem. The statement proven in 1934 was weaker. As evidence for this, you can see that Erdos posed this problem after 1934.

No it's not like saying that at all, which is why Open AI have a credit on the paper.

There are closely related "MHV amplitudes" which naively obey a really complicated formula, but for which there famously also exists a much simpler "Parke-Taylor formula". Alfredo had derived a complicated expression for these new "single-minus amplitudes" and we were hoping we could find an analogue of the simpler "Parke-Taylor formula" for them.

And even if it were, calculators (computers) were world-changing technology when they were new.

Open AI have a credit on the paper because it is marketing.

Thank you for taking the time to reply, I see you might have already answered this elsewhere so it's much appreciated.

Lol Okay