2/13/2026 at 7:36:16 PM
The headline may make it seem like AI just discovered some new result in physics all on its own, but reading the post, humans started off trying to solve some problem, it got complex, GPT simplified it and found a solution with the simpler representation. It took 12 hours for GPT pro to do this. 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.by outlace
2/13/2026 at 7:48:24 PM
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
by CGMthrowaway
2/13/2026 at 8:48:42 PM
> I think this was all already figured out in 1986 though
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.
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.
by godelski
2/13/2026 at 8:56:19 PM
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 distributionalby CGMthrowaway
2/13/2026 at 9:08:21 PM
> simplest nonzero amplitudes have two minus helicities while one-minus amplitudes vanishSorry but I just have to point out how this field of maths read like Star Trek technobabble too me.
by rightbyte
2/13/2026 at 10:19:16 PM
Where do you think Star Trek got its technobabble from?by layer8
2/13/2026 at 9:12:17 PM
https://www.youtube.com/watch?v=cn4fW0EInqwby athrowaway3z
2/13/2026 at 10:05:31 PM
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?by nyc_data_geek1
2/13/2026 at 10:14:25 PM
you’re not, and you might have a slight reading comprehension problemby throwuxiytayq
2/13/2026 at 8:47:13 PM
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.
by btown
2/13/2026 at 9:43:58 PM
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.by lupsasca
2/13/2026 at 8:26:08 PM
You should probably email the authors if you think that's true. I highly doubt they didn't do a literature search first though...by woeirua
2/13/2026 at 8:33:00 PM
You should be more skeptical of marketing releases like this. This is an advertisement.by emp17344
2/13/2026 at 8:49:24 PM
They also reference Parke and Taylor. Several times...by godelski
2/13/2026 at 8:48:22 PM
Don't underestimate the willingness of physicists to skimp on literature review.by suuuuuuuu
2/13/2026 at 9:12:16 PM
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.by baq
2/13/2026 at 8:15:37 PM
Still pretty awesome though, if you ask me.by ericmay
2/13/2026 at 8:25:07 PM
I think even “non-intelligent” solver like Mathematica is cool - so hell yes, this is cool.by fsloth
2/13/2026 at 8:45:07 PM
Big difference between “derives new result” and “reproduces something likely in its training dataset”.by _aavaa_
2/13/2026 at 9:26:43 PM
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.by torginus
2/13/2026 at 8:10:44 PM
> but I haven’t been to get them to do something totally out of distribution yet from first principlesCan 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.
by randomtoast
2/13/2026 at 8:59:32 PM
> 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.
by davorak
2/13/2026 at 8:15:09 PM
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.
by dotancohen
2/13/2026 at 8:49:22 PM
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!)
by Paracompact
2/13/2026 at 9:07:24 PM
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.)
by _alternator_
2/13/2026 at 9:50:17 PM
> Critique of absolute time and space of Newtonian physics was already well underway
> Many of the phenomena that relativity would later explain under a consistent framework already had independent quasi-explanations hinting at the more universal theory.
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
by godelski
2/13/2026 at 8:58:47 PM
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.
by bananaflag
2/13/2026 at 8:19:46 PM
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.by johnfn
2/13/2026 at 8:36:24 PM
That's not exactly true. Lorentz contraction is a clear antecedent to special relativity.by poplarsol
2/13/2026 at 8:39:15 PM
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...
by lamontcg
2/13/2026 at 8:57:19 PM
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."
:)
by Paracompact
2/13/2026 at 8:39:40 PM
Go enough shoulders down, and someone had to have been the first giant.by tjr
2/13/2026 at 9:27:21 PM
Probably not homo sapiens.. other hominids older than us developed a lot of technologyby nextaccountic
2/13/2026 at 8:44:58 PM
Pythagoras is the turtle.by pram
2/13/2026 at 9:36:14 PM
Pythagoras learned from Egyptians that have been largely erased by euro/western narratives of superiority.by TheCycoONE
2/13/2026 at 8:31:15 PM
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.
by CooCooCaCha
2/13/2026 at 9:37:42 PM
> Can humans actually do that?
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.
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 partsby godelski
2/13/2026 at 7:42:16 PM
"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.
by emil-lp
2/13/2026 at 8:07:30 PM
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.by Sharlin
2/13/2026 at 8:23:21 PM
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.
by buttered_toast
2/13/2026 at 9:43:23 PM
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.by lupsasca
2/13/2026 at 9:48:47 PM
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?by buttered_toast
2/13/2026 at 9:57:50 PM
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.by lupsasca
2/13/2026 at 10:00:49 PM
Thank you for taking the time to reply, I see you might have already answered this elsewhere so it's much appreciated.by buttered_toast
2/13/2026 at 8:43:17 PM
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.
by lamontcg
2/13/2026 at 8:55:23 PM
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.by Sharlin
2/13/2026 at 8:20:40 PM
hey, GPT, solve this tough conjecture I've read about on Quanta. make no mistakesby slopusila
2/13/2026 at 8:32:18 PM
[dead]by co_king_3
2/13/2026 at 8:42:25 PM
"Hey GPT thanks for the result. But is it actually true?"by terminalbraid
2/13/2026 at 9:38:38 PM
"Grad Student did this". Co-authored by <Famous advisor 1>, <Famous advisor 2>, <Famous advisor 3>.Is this so different?
by jmalicki
2/13/2026 at 8:38:32 PM
[dead]by hgfda
2/13/2026 at 7:48:04 PM
The paper has all those prominent institutions who acknowledge the contribution so realistically, why would you be skeptical ?by famouswaffles
2/13/2026 at 7:54:22 PM
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.
by kristopolous
2/13/2026 at 7:56:45 PM
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
by famouswaffles
2/13/2026 at 7:59:53 PM
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?by name_taken_duh
2/13/2026 at 8:19:08 PM
> 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.
by palmotea
2/13/2026 at 8:34:33 PM
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
by kristopolous
2/13/2026 at 7:55:29 PM
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?by Refreeze5224
2/13/2026 at 8:00:04 PM
It's a stupid point then. Are you able to work with a world leading physicist to any significant degree? Noby famouswaffles
2/13/2026 at 8:37:24 PM
It's like saying: calculator drives new result in theoretical physics(In the hands of leading experts.)
by emil-lp
2/13/2026 at 8:45:47 PM
No it's not like saying that at all, which is why Open AI have a credit on the paper.by famouswaffles
2/13/2026 at 9:21:24 PM
Open AI have a credit on the paper because it is marketing.by camdenreslink
2/13/2026 at 9:53:40 PM
Lol Okayby famouswaffles
2/13/2026 at 10:02:32 PM
And even if it were, calculators (computers) were world-changing technology when they were new.by not_kurt_godel
2/13/2026 at 10:01:02 PM
> 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).
by slibhb
2/13/2026 at 8:48:03 PM
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.
by stouset
2/13/2026 at 10:36:54 PM
We are already at stage 3 for software development and arguably step 4by empath75
2/13/2026 at 9:03:13 PM
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".by TGower
2/13/2026 at 9:48:21 PM
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.
by stouset
2/13/2026 at 9:19:10 PM
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.
by baq
2/13/2026 at 9:31:00 PM
> 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...
by NitpickLawyer
2/13/2026 at 8:56:05 PM
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.by bluecalm
2/13/2026 at 9:20:00 PM
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?
by hellisad
2/13/2026 at 10:02:36 PM
Insert perfunctory HN reply of "but do humans ever do anything totally out of distribution from first principles?"(This is deep)
by getnormality
2/13/2026 at 10:15:34 PM
What does a 12-hour solution cost an OpenAI customer?by tedd4u
2/13/2026 at 8:19:24 PM
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?by epolanski
2/13/2026 at 8:22:28 PM
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.by javier123454321
2/13/2026 at 8:23:39 PM
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)by lovecg
2/13/2026 at 8:06:28 PM
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...
by ctoth
2/13/2026 at 7:44:25 PM
I don't want to be rude but like, maybe you should pre-register some statement like "LLMs will not be able to do X" in some concrete domain, because I suspect your goalposts are shifting without you noticing.We're talking about significant contributions to theoretical physics. You can nitpick but honestly go back to your expectations 4 years ago and think — would I be pretty surprised and impressed if an AI could do this? The answer is obviously yes, I don't really care whether you have a selective memory of that time.
by bpodgursky
2/13/2026 at 7:57:02 PM
I don't know enought about theoretical physics: what makes it a significant contribution there?by RandomLensman
2/13/2026 at 8:29:24 PM
It's a nontrivial calculation valid for a class of forces (e.g. QCD) and apparently a serious simplification to a specific calculation that hadn't been completed before. But for what it's worth, I spent a good part of my physics career working in nucleon structure and have not run across the term "single minus amplitudes" in my memory. That doesn't necessarily mean much as there's a very broad space work like this takes place in and some of it gets extremely arcane and technical.One way I gauge the significance of a theory paper are the measured quantities and physical processes it would contribute to. I see none discussed here which should tell you how deep into math it is. I personally would not have stopped to read it on my arxiv catch-up
https://arxiv.org/list/hep-th/new
Maybe to characterize it better, physicists were not holding their breath waiting for this to get done.
by terminalbraid
2/13/2026 at 8:33:49 PM
Thank you!by RandomLensman
2/13/2026 at 8:20:58 PM
Not every contribution has immediate impact.by epolanski
2/13/2026 at 8:26:45 PM
That doesn't answer the question. That statement just admits "maybe" which isn't helpful or insightful to answering it.by terminalbraid
2/13/2026 at 8:12:32 PM
I never said LLMs will not be able to do X. I gave my summary of the article and my anecdotal experiences with LLMs. I have no LLM ideology. We will see what tomorrow brings.by outlace
2/13/2026 at 7:48:48 PM
> We're talking about significant contributions to theoretical physics.Whoever wrote the prompts and guided ChatGPT made significant contributions to theoretical physics. ChatGPT is just a tool they used to get there. I'm sure AI-bloviators and pelican bike-enjoyers are all quite impressed, but the humans should be getting the research credit for using their tools correctly. Let's not pretend the calculator doing its job as a calculator at the behest of the researcher is actually a researcher as well.
by nozzlegear
2/13/2026 at 7:51:36 PM
If this worked for 12 hours to derive the simplified formula along with its proof then it guided itself and made significant contributions by any useful definition of the word, hence Open AI having an author credit.by famouswaffles
2/13/2026 at 7:56:55 PM
> hence Open AI having an author credit.How much precedence is there for machines or tools getting an author credit in research? Genuine question, I don't actually know. Would we give an author credit to e.g. a chimpanzee if it happened to circle the right page of a text book while working with researchers, leading them to a eureka moment?
by nozzlegear
2/13/2026 at 8:24:40 PM
>How much precedence is there for machines or tools getting an author credit in research?For a datum of one, the mathematician Doron Zeilberger give credit to his computer Shalosh B. Ekhad on select papers.
https://medium.com/@miodragpetkovic_24196/the-computer-a-mys...
https://sites.math.rutgers.edu/~zeilberg/akherim/EkhadCredit...
by floxy
2/13/2026 at 8:39:26 PM
Interesting (and an interesting name for the computer too), thanks!by nozzlegear
2/13/2026 at 8:30:36 PM
Not exactly the same thing, but I know of at least two professors that would try to list their cats as co-authors:by steveklabnik
2/13/2026 at 8:43:22 PM
That is great, thank you!by nozzlegear
2/13/2026 at 8:10:52 PM
I have seem stuff like "you can use my program if you will make me a co-author".That usually comes up with some support usually.
by kuboble
2/13/2026 at 8:08:33 PM
>How much precedence is there for machines or tools getting an author credit in research?Well what do you think ? Do the authors (or a single symbolic one) of pytorch or numpy or insert <very useful software> typically get credits on papers that utilize them heavily? Well Clearly these prominent institutions thought GPT's contribution significant enough to warrant an Open AI credit.
>Would we give an author credit to e.g. a chimpanzee if it happened to circle the right page of a text book while working with researchers, leading them to a eureka moment?
Cool Story. Good thing that's not what happened so maybe we can do away with all these pointless non sequiturs yeah ? If you want to have a good faith argument, you're welcome to it, but if you're going to go on these nonsensical tangents, it's best we end this here.
by famouswaffles
2/13/2026 at 8:26:59 PM
> Well what do you think ? Do the authors (or a single symbolic one) of pytorch or numpy or insert <very useful software> typically get credits on papers that utilize them heavily ?I don't know! That's why I asked.
> Well Clearly these prominent institutions thought GPT's contribution significant enough to warrant an Open AI credit.
Contribution is a fitting word, I think, and well chosen. I'm sure OpenAI's contribution was quite large, quite green and quite full of Benjamins.
> Cool Story. Good thing that's not what happened so maybe we can do away with all these pointless non sequiturs yeah ? If you want to have a good faith argument, you're welcome to it, but if you're going to go on these nonsensical tangents, it's best we end this here.
It was a genuine question. What's the difference between a chimpanzee and a computer? Neither are humans and neither should be credited as authors on a research paper, unless the institution receives a fat stack of cash I guess. But alas Jane Goodall wasn't exactly flush with money and sycophants in the way OpenAI currently is.
by nozzlegear
2/13/2026 at 8:33:58 PM
>I don't know! That's why I asked.If you don't read enough papers to immediately realize it is an extremely rare occurrence then what are you even doing? Why are you making comments like you have the slightest clue of what you're talking about? including insinuating the credit was what...the result of bribery?
You clearly have no idea what you're talking about. You've decided to accuse prominent researchers of essentially academic fraud with no proof because you got butthurt about a credit. You think your opinion on what should and shouldn't get credited matters ? Okay
I've wasted enough time talking to you. Good Day.
by famouswaffles
2/13/2026 at 8:46:28 PM
Do I need to be credentialed to ask questions or point out the troubling trend of AI grift maxxers like yourself helping Sam Altman and his cronies further the myth of AGI by pretending a machine is a researcher deserving of a research credit? This is marketing, pure and simple. Close the simonw substack for a second and take an objective view of the situation.by nozzlegear
2/13/2026 at 8:24:55 PM
it's called ethics and research integrity. not crediting GPT would be a form of misrepresentationby slopusila
2/13/2026 at 8:28:58 PM
Would it? I think there's a difference between "the researchers used ChatGPT" and "one of the researchers literally is ChatGPT." The former is the truth, and the latter is the misrepresentation in my eyes.I have no problem with the former and agree that authors/researchers must note when they use AI in their research.
by nozzlegear
2/13/2026 at 8:31:53 PM
now you are debating exactly how GPT should be credited. idk, I'm sure the field will make up some guidancefor this particular paper it seems the humans were stuck, and only AI thinking unblocked them
by slopusila
2/13/2026 at 8:42:34 PM
> now you are debating exactly how GPT should be credited. idk, I'm sure the field will make up some guidanceIn your eyes maybe there's no difference. In my eyes, big difference. Tools are not people, let's not further the myth of AGI or the silly marketing trend of anthropomorphizing LLMs.
by nozzlegear
2/13/2026 at 7:58:11 PM
If a helicopter drops someone off on the top of Mount Everest, it's reasonable to say that the helicopter did the work and is not just a tool they used to hike up the mountain.by bpodgursky
2/13/2026 at 8:00:51 PM
Who piloted the helicopter in this scenario, a human or chatgpt? You'd say the pilot dropped them off in a helicopter. The helicopter didn't fly itself there.by nozzlegear
2/13/2026 at 8:10:17 PM
“They have chosen cunning instead of belief. Their prison is only in their minds, yet they are in that prison; and so afraid of being taken in that they cannot be taken out.”― C.S. Lewis, The Last Battle
by bpodgursky
2/13/2026 at 8:59:49 PM
"For me, it is far better to grasp the universe as it really is than to persist in delusion, however satisfying and reassuring."— Carl Sagan
by nozzlegear
2/13/2026 at 9:09:45 PM
I read the narnia series many times as a kid and this one stuck with me, I didn't prompt for it.I have no real way to demonstrate that I'm telling the truth, but I am ¯\_(ツ)_/¯
by bpodgursky
2/13/2026 at 9:43:53 PM
Sorry for the assumption. For what it's worth, I read one of Sagan's books last year, but pulled the quote from Goodreads :Pby nozzlegear
2/13/2026 at 7:48:32 PM
Is every new thing not just combinations of existing things? What does out of distribution even mean? What advancement has ever made that there wasn’t a lead up of prior work to it? Is there some fundamental thing that prevents AI from recombining ideas and testing theories?by bottlepalm
2/13/2026 at 8:21:37 PM
> Is every new thing not just combinations of existing things?If all ideas are recombinations of old ideas, where did the first ideas come from? And wouldn't the complexity of ideas be thus limited to the combined complexity of the "seed" ideas?
I think it's more fair to say that recombining ideas is an efficient way to quickly explore a very complex, hyperdimensional space. In some cases that's enough to land on new, useful ideas, but not always. A) the new, useful idea might be _near_ the area you land on, but not exactly at. B) there are whole classes of new, useful ideas that cannot be reached by any combination of existing "idea vectors".
Therefore there is still the necessity to explore the space manually, even if you're using these idea vectors to give you starting points to explore from.
All this to say: Every new thing is a combination of existing things + sweat and tears.
The question everyone has is, are current LLMs capable of the latter component. Historically the answer is _no_, because they had no real capacity to iterate. Without iteration you cannot explore. But now that they can reliably iterate, and to some extent plan their iterations, we are starting to see their first meaningful, fledgling attempts at the "sweat and tears" part of building new ideas.
by fpgaminer
2/13/2026 at 10:26:03 PM
Well, what exactly an “idea” is might be a little unclear, but I don’t think it clear that the complexity of ideas that result from combining previously obtained ideas would be bounded by the complexity of the ideas they are combinations of.Any countable group is a quotient of a subgroup of the free group on two elements, iirc.
There’s also the concept of “semantic primes”. Here is a not-quite correct oversimplification of the idea: Suppose you go through the dictionary and one word at a time pick a word whose definition includes only other words that are still in the dictionary, and removing them. You can also rephrase definitions before doing this, as long as it keeps the same meaning. Suppose you do this with the goal of leaving as few words in it as you can. In the end, you should have a small cluster of a bit over 100 words, in terms of which all the other words you removed can be indirectly defined. (The idea of semantic primes also says that there is such a minimal set which translates essentially directly* between different natural languages.)
I don’t think that says that words for complicated ideas aren’t like, more complicated?
by drdeca
2/13/2026 at 8:47:55 PM
"Sweat and tears" -> exploration and the training signal for reinforcement learning.by red75prime
2/13/2026 at 8:10:36 PM
For example, ever since the first GPT 4 I’ve tried to get LLM’s to build me a specific type of heart simulation that to my knowledge does not exist anywhere on the public internet (otherwise I wouldn’t try to build it myself) and even up to GPT 5.3 it still cannot do it.But I’ve successfully made it build me a great Poker training app, a specific form that also didn’t exist, but the ingredients are well represented on the internet.
And I’m not trying to imply AI is inherently incapable, it’s just an empirical (and anecdotal) observation for me. Maybe tomorrow it’ll figure it out. I have no dogmatic ideology on the matter.
by outlace
2/13/2026 at 8:27:23 PM
Just wait until LLMs are fast and cheap enough to be run in a breadth first search kind of way, with "fuzzy" pruning.by amelius
2/13/2026 at 7:57:37 PM
[flagged]by verdverm
2/13/2026 at 8:02:47 PM
Absolutely no way this is true right? Ilya left around the time 4o was released. I can't imagine they haven't had a single successful run since then.by buttered_toast
2/13/2026 at 8:21:58 PM
When's the last time they talked about it?I heard this from people who know more than me
by verdverm
2/13/2026 at 8:25:18 PM
Can't say, just seems implausible, but I am a nobody anyways ¯\_(ツ)_/¯by buttered_toast
2/13/2026 at 10:06:36 PM
I'm pretty sure it is widely known that the early 5.x series were built from 4.5 (unreleased). It seems more plausible the 5.x series is still in that continuation.For some extra context, pre-training is ~1/3 of the training, where it gains the basic concepts of how tokens go together. Mid & late training are where you instill the kinds of anthropic behaviors we see today. I expect pre-training to increasingly become a lower percentage of overall training, putting aside any shifts of what happens in each phase.
So to me, it is plausible they can take the 4.x pre-training and keep pushing in the later phases. There is a lot of results out there to show scaling laws (limits) have not peaked yet. I would not be surprised to learn that Gemini 3 Deep Research had 50% late-training / RL
by verdverm
2/13/2026 at 10:19:20 PM
Okay I see what you mean, and yeah that sounds reasonable too. Do you have any context on that first part? I would like to know more about how/why they might not have been able to pursue more training runs.by buttered_toast