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  > 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.

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.

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.

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

Still pretty awesome though, if you ask me.

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.

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.

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

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.

  > 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 parts

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.

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

Is this so different?

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

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".

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

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.

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.

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)

I don't know enought about theoretical physics: what makes it a significant contribution there?

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.

> 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.

> 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.

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.

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

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

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.

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.

> 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.

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.

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.

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?

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

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.

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.

I believe Gemini holds the moat now.