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
> 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.
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.
Slightly OT, but wasn't this supposed to be largely solved with amplituhedrons?
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.
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.
These have been identified as various things. Eureka moments, strokes of genius, out of the box thinking, lateral thinking.
LLMs have not shown to be capable of this. They might be in the future, but they havent yet
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.
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.
So that's actually 2 different regimes on how to proceed. Both are useful but arguably breaking off of the current paradigm is much harder and thus rare.
There are genuine creative insights that come from connecting two known semantic spaces in a way that wasn't obvious before (e.g, novel isomorphism). It is very conceivable that LLMs could make this kind of connection, but we haven't really seen a dramatic form of this yet. This kind of connection can lead to deep, non-trivial insights, but whether or not it is "out-of-distribution" is harder to answer in this case.
> 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 partsFive 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.
In chess, there's a clear goal: beat the game according to this set of unambiguous rules.
In science, the goals are much more diffuse, and setting those in the first place is what makes a scientist more or less successful, not so much technical ability. It's a very hierarchical field where permanent researchers direct staff (postdocs, research scientists/engineers), direct grad students. And it's at the bottom of the pyramid where the technical ability is the most relevant/rewarded.
Research is very much a social game, and I think replacing it with something run by LLMs (or other automatic process) is much more than a technical challenge.
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.
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.
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.
Is this so different?
LLMs surpassed the average human a long time ago IMO. When LLMs fail to measure up to humans, it's that they fail to measure up against human experts in a given field, not the Average Joe.
We are surrounded by NPCs.
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).
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?
over long periods of time, checklists are the biggest thing, so the LLM can track whats already done and whats left. after a compact, it can pull the relevant stuff back up and make progress.
having some level or hierarchy is also useful - requirements, high level designs, low level designs, etc
The real question is, what does it cost OpenAI? I'm pretty sure both their plans are well below cost, at least for users who max them out (and if you pay $200 for something then you'll probably do that!). How long before the money runs out? Can they get it cheap enough to be profitable at this price level, or is this going to be "get them addicted then jack it up" kind of strategy?
Agree with this. I’ve been trying to make LLMs come up with creative and unique word games like Wordle and Uncrossy (uncrossy.com), but so far GPT-5.2 has been disappointing. Comparatively, Opus 4.5 has been doing better on this.
But it’s good to know that it’s breaking new ground in Theoretical Physics!
It seems to me that all “new ideas” are basically linear combinations of existing things with exceeding rare exceptions…
Maybe Godel’s Incompleteness?
Darwinian evolution?
General Relativity?
Buddhist non-duality?
Aren't most new things linear combinations of existing things (up to a point)?
Thanks for the summary; but this is a huge hand-wave. was GPT Pro just spinning for 12 hours and returend 42?!
But it's worth thinking more about this. What gives humans the ability to discover "new things"? I would say it's due to our interaction with the universe via our senses, and not due to some special powers intrinsic to our brains that LLMs lack. And the thing is, we can feed novel measurements to LLMs (or, eventually, hook them up to camera feeds to "give them senses")
[0]: https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
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.
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.
There are in fact ways to directly quantify this, if you are training e.g. a self-supervised anomaly-detection model.
Even with modern models not trained in that manner, looking at e.g. cosine distances of embeddings of "novel" outputs could conceivably provide objective evidence for "out-of-distribution" results. Generally, the embeddings of out-of-distribution outputs will have a large cosine (or even Euclidean) distance from the typical embedding(s). Just, most "out-of-distribution" outputs will be nonsense / junk, so, searching for weird outputs isn't really helpful, in general, if your goal is useful creativity.
It's easy to fall into a negative mindset because the justification is real and what we see is just the beginning.
Obviously we are not at a point where developers aren't needed. But One developer can do more. And that is a legitimate reason to higher less developers.
The impending reality of the upward moving trendline is that AI becomes so capable that it can replace the majority of developers. That future is so horrifying that people need to scaffold logic to unjustifiy it.
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.
Like I have compassion, but I can't healthily respect people who try so hard to rewrite reality so that the future isn't so horrifying. I'm a SWE and I'm affected too, but it's not like I'm going to lie to myself about what's happening.
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 thing about spin and AI hype (besides being trivially easy to write) is that is isn't even trying to be objective. It would help if a lot of these articles would more carefully lay out what is actually surprising, and what is not, given current tech and knowledge.
Only a fool would think we aren't potentially on the verge of something truly revolutionary here. But only a fool would also be certain that the revolution has already happened, or that e.g. AGI is necessarily imminent.
The reason HN has value is because you can actually see some specifics of the matter discussed, and, if you are lucky, an expert even might join in to qualify everything. But pointing out "how interesting that there are extremes to this" is just engagement bait.
Really? Is that happening in this thread because I can barely see it. Instead you have a bunch of asinine comments butthurt about acknowledging a GPT contribution that would have been acknowledged any day had a human done it.
>they know more about this than Fields medalist Terence Tao, who maintains this list showing that, yes, though these are not interesting proofs to most modern mathematicians, LLMs are a major factor in a tiny minority of these mostly-not-very-interesting proofs
This is part of the problem really. Your framing is disingenuous and I don't really understand why you feel the need to downplay it so. They are interesting proofs. They are documented for a reason. It's not cutting edge research, but it is LLMs contributing meaningfully to formal mathematics, something that was speculative just years ago.
This sentence sounds contradictory. You're a fool to not think we're on the verge of something revolutionary and you are a fool if you think something revolutionary like AGI is on the verge of happening?
But to your point if "revolutionary" and "agi" are different things, I'm certain the "revolution" has already happened. ChatGPT was the step function change and everything else is just following the upwards trendline post release of chatGPT.
Anecdotally I would say 50% of developers never code things by hand anymore. That is revolutionary in itself and by the statement itself it has already literally happened.
And in this case "derives a new result in theoretical physics" is again overstating things, it's closer to "simplify and propose a more general form for a previously worked out sequence of amplitudes" which sounds less magical, and closer to something like what Mathematica could do, or an LLM-enhanced symbolic OEIS. Obviously still powerful and useful, but less hype-y.
How is this different than a new result? Many a careers in academia are built on simplifying mathematics.
It's interesting to me that whenever AI gets a bunch of instructions from a reasonably bright person who has a suspicion about something, can point at reasons why, but not quite put their finger on it, we want to credit the AI for the insight.
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.
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.
Yet, if some student or child achieved the same – under equal supervision – we would call him the next Einstein.
There are simple limitations that follow from these basic facts (or which follow with e.g. extreme but not 100% certainty), such that many experts openly state that e.g. LLMs have serious limitations, but, still, despite all this, you get some very extreme claims about capabilities, from supporters, that are extremely hard to reconcile with these basic and indisputable facts.
That, and the massive investment and financial incentives means that the counter-reaction is really quite rational (but still potentially unwarranted, in some/many practical cases).
There is no loud, moderate voice. It makes me very tired of the blasting rhetoric that invades _every_ space.
It reminds me of an episode of Star Trek, "The Measure of a Man" I think it's called, where it is argued that Data is just a machine and Picard tries to prove that no he is a life form.
And the challenge is, how do you prove that?
Every time these LLMs get better, the goalposts move again.
It makes me wonder, if they ever did become sentient, how would they be treated?
It's seeming clear that they would be subject to deep skepticism and hatred much more pervasive and intense than anything imagined in The Next Generation.
Wait, so this is now a contest (or maybe war) that LLMs are supposed to win?
Wild.
What I question here is OpenAI's article: it could be way more generous towards the reader.
One group of people saying every amazing breakthrough "doesn't count" because the AI didn't put a cherry on top. Another group of people saying humans are obsolete, I just wrote a web browser with AI bro.
There are some voices out there that are actually examining the boundaries, possibilities and limitations. A lot of good stuff like that makes it onto HN but then if you open the comments it's just intellectual dregs. Very strange.
ISTR there was a similar phenomenon with cryptocurrency. But with that it was always clear the fog of bullshit would blow away sooner or later. But maybe if it hadn't been there, a load of really useful stuff could have come out of the crypto hype wave? Anyway, AI isn't gonna blow over like crypto did. I guess we have more of a runway to grow out of this infantile phase.
They never surrender.
No one cares about how "AGI" or whatever the fuck term or internet-argument goalpost you cared about X months ago was. Everyone cares about what current tech can do NOW, and under what conditions, and when it fails catastrophically. That is all that matters.
So, refining the conditions of an LLM win (or loss) is all that matters (not who wins or loses depending on some particular / historical refinement). Complaining that some people see some recent result as a loss (or win) is just completely failing to understand the actual game being played / what really matters here.
Take a look at this entire thread. Everyone and I mean everyone is talking as if AI is some sort of fraud and everything is just hype. But then this thread is all against, AI, I mean all of it. If anything the Anti-hype around AI is what's flooding the world right now. If AI hype was through the roof we'd see the opposite effect on HN.
I think it's a strange contradiction in the human mind. At work outside of HN, what I see is roughly 50-60% of developers no longer code by hand. They all use AI. Then they come onto HN and they start Anti-hyping it. It's universal. They use it and they're against it at the same time.
The contradiction is strange, but it also makes sense because AI is a thing that is attacking what programmers take pride in. Most programmers are so proud of their abilities and intelligence as it relates to their jobs and livelihood. AI is on a trendline of replacing this piece by piece. It makes perfect sense for them to talk shit but at the same time they have to use it to keep up with the competition.
When I use GPT 5.2 Thinking Extended, it gave me the impression that it's consistent enough/has a low enough rate of errors (or enough error correcting ability) to autonomously do math/physics for many hours if it were allowed to [but I guess the Extended time cuts off around 30 minute mark and Pro maybe 1-2 hours]. It's good to see some confirmation of that impression here. I hope scientists/mathematicians at large will be able to play with tools which think at this time-scale soon and see how much capabilities these machines really have.
This result reminded me of the C compiler case that Anthropic posted recently. Sure, agents wrote the code for hours but there was a human there giving them directions, scoping the problem, finding the test suites needed for the agentic loops to actually work etc etc. In general making sure the output actually works and that it's a story worth sharing with others.
The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding. It works great for creating impressions and building brand value but also does a disservice to the actual researchers, engineers and humans in general, who do the hard work of problem formulation, validation and at the end, solving the problem using another tool in their toolbox.
>[...]
>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?
A website that cost hundreds of thousands of dollars in 2000 could be replaced by a wordpress blog built in an afternoon by a teenager in 2015. Did that kill web development? No, it just expanded what was worth building
Yes, but this assumes a finite amount of software that people and businesses need and want. Will AI be the first productivity increase where humanity says ‘now we have enough’? I’m skeptical.
Maybe it requires fundamentally changing or economic systems? Who knows what the solution is, but the problem is most definitely rooted in lack of initiative by our representatives and an economic system that doesn't accommodate us for when shit inevitably hits the fan with labor markets.
I'm curious why you think I'm acting like it's all or nothing. What I was trying to communicate is the exact opposite, that it's not all or nothing. Maybe it's the way I articulate things, I'm genuinely interested what makes it sound like this.
That, of course, assumes that there are 9 other projects that are both known (or knowable) and worth doing. And in the case of Uber/Lyft drivers, there's a skillset mismatch between the "deprecated" jobs and their replacements.
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.
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.
To think that whatever the AI is capable of solving is (and forever will be) the frontier of all problems is deeply delusional. AI got good at generating code, but it still can't even do a fraction of what the human brain can do.
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?
I worry we're not producing as many of those as we used to
The text of the post is much more honest. The title is where the dishonesty is.
(35)-(38) are the AI-simplified versions of (29)-(32). Those earlier formulae look formidable to simplify by hand, but they are also the sort of thing you'd try to use a computer algebra system for.
I'm willing to (begrudgingly) admit the possibility for AI to do novel work, but this particular result does not seem very impressive.
I picture ChatGPT as the rich kid whose parents privately donated to a lab to get their name on a paper for college admissions. In this case, I don't think I'm being too cynical in thinking that something similar is happening here and that the role of AI in this result is being well overplayed.
Snow + stick + need to clean driveway = snow shovel. Snow shovel + hill + desire for fun = sled
At one point people were arguing that you could never get "true art" from linear programs. Now you get true art and people are arguing you can't get magical flashes of insight. The will to defend human intelligence / creativity is strong but the evidence is weak.
Happy Valentine's day to those who celebrate btw <3
So I would read this (with more information available) with less emphasize on LLM discovering new result. The title is a little bit misleading but actually "derives" being the operative word here so it would be technically correct for people in the field.
[1] https://en.wikipedia.org/wiki/List_of_physical_constants
They evaluate papers that look interesting and should be looked at more deeply. Then, research ideas as much as they can.
Then flag for human review the real possible breakthroughs.
;)
Okay read it: Yep Induction. It already had the answer.
Don't get me wrong, I love Induction... but we aren't having any revolutions in understanding with Induction.
I expect lots of derivations (new discoveries whose pieces were already in place somewhere, but no one has put them together).
In this case, the human authors did the thinking and also used the LLM, but this could happen without the original human author too (some guy posts some partial on the internet, no one realizes is novel knowledge, gets reused by AI later). It would be tremendously nice if credit was kept in such possible scenarios.
Any reason to believe that public versions of GPT-5.2 could have accomplished this task? "scaffolded" is a very interesting word choice
My personal opinion is that things will only accelerate from here.
That is what one of the author says. This doesn't quite fit to the headline of the post.
Couldn't is an immensely high bar in this context, didn't seems more appropriate and renders this whole thing slightly less exciting.
I'm not blaming the model here, but Python is much easier to read and more universal than math notation in most cases (especially for whatever's going on at the bottom of page four). I guess I'll have one translate the PDF.
Not saying they're lying, but I'm sure it's exaggerated in their own report.
New Honda Civic discovered Pacific Ocean!
New F150 discovers Utah Salt Flats!
Sure it took humans engineering and operating our machines, but the car is the real contributor here!
I am generally very skeptical about work on this level of abstraction. only after choosing Klein signature instead of physical spacetime, complexifying momenta, restricting to a "half-collinear" regime that doesn't exist in our universe, and picking a specific kinematic sub-region. Then they check the result against internal consistency conditions of the same mathematical system. This pattern should worry anyone familiar with the replication crisis. The conditions this field operates under are a near-perfect match for what psychology has identified as maximising systematic overconfidence: extreme researcher degrees of freedom (choose your signature, regime, helicity, ordering until something simplifies), no external feedback loop (the specific regimes studied have no experimental counterpart), survivorship bias (ugly results don't get published, so the field builds a narrative of "hidden simplicity" from the survivors), and tiny expert communities where fewer than a dozen people worldwide can fully verify any given result.
The standard defence is that the underlying theory — Yang-Mills / QCD — is experimentally verified to extraordinary precision. True. But the leap from "this theory matches collider data" to "therefore this formula in an unphysical signature reveals deep truth about nature" has several unsupported steps that the field tends to hand-wave past.
Compare to evolution: fossils, genetics, biogeography, embryology, molecular clocks, observed speciation — independent lines of evidence from different fields, different centuries, different methods, all converging. That's what robust external validation looks like. "Our formula satisfies the soft theorem" is not that.
This isn't a claim that the math is wrong. It's a claim that the epistemic conditions are exactly the ones where humans fool themselves most reliably, and that the field's confidence in the physical significance of these results outstrips the available evidence.
I wrote up a more detailed critique in a substack: https://jonnordland.substack.com/p/the-psychologists-case-ag...
Theoretical physics is throwing a lot of stuff at the wall and theory crafting to find anything that might stick a little. Generation might actually be good there, even generation that is "just" recombining existing ideas.
I trust physicists and mathematicians to mostly use tools because they provide benefit, rather than because they are in vogue. I assume they were approached by OpenAI for this, but glad they found a way to benefit from it. Physicists have a lot of experience teasing useful results out of probabilistic and half broken math machines.
If LLMs end up being solely tools for exploring some symbolic math, that's a real benefit. Wish it didn't involve destroying all progress on climate change, platforming truly evil people, destroying our economy, exploiting already disadvantaged artists, destroying OSS communities, enabling yet another order of magnitude increase in spam profitability, destroying the personal computer market, stealing all our data, sucking the oxygen out of investing into real industry, and bold faced lies to all people about how these systems work.
Also, last I checked, MATLAB wasn't a trillion dollar business.
Interestingly, the OpenAI wrangler is last in the list of Authors and acknowledgements. That somewhat implies the physicists don't think it deserves much credit. They could be biased against LLMs like me.
When Victor Ninov (fraudulently) analyzed his team's accelerator data using an existing software suite to find a novel SuperHeavy element, he got first billing on the authors list. Probably he contributed to the theory and some practical work, but he alone was literate in the GOOSY data tool. Author lists are often a political game as well as credit, but Victor got top billing above people like his bosses, who were famous names. The guy who actually came up with the idea of how to create the element, in an innovative recipe that a lot of people doubted, was credited 8th
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.83...
Basically, if you are small enough you can move forwards and backwards in time, from the moment you were put into a superposition, or entangled, until you interact with an object too large to ignore the emergent effects of time and gravity. This is 'being observed' and 'collapsing the wave function'. You occupy all possible positions in space as defined by the probability of you being there. Once observed, you move forward in linear time again and the last route you took is the only one you ever took even though that route could be affected by interference with other routes you took that now no longer exist. When in this state there is no 'before' or 'after' so the delayed choice experiment is simply an illusion caused by our view of time, and there is no delay, the choice and result all happen together.
With entanglement, both particles return to the entanglement point, swap places and then move to the current moment and back again, over and over. They obey GR, information always travels under the speed of light (which to the photon is infinite anyway), so there is no spooky action at a distance, it is sub-lightspeed action through time that has the illusion of being instant to entities stuck in linear time.
It then went on to talk about how mass creates time, and how time is just a different interpretation of gravity leading it to fully explain how a black hole switches time and space, and inwards becomes forwards in time inside the event horizon. Mass warps 4D (or more) space. That is gravity, and it is also time.