Hey everyone, welcome back to My Weird Prompts. It is February fourteenth, twenty twenty six, and while most people are out for Valentine's Day, we are hunkered down in the basement because the world of artificial intelligence just dropped a massive bombshell. I am Corn, and as always, I am joined by my brother, housemate, and resident technical deep-diver, Herman Poppleberry.
Reporting for duty, Corn. And happy Valentine's Day to you, I guess, though I think we both know that a breakthrough in quantum chromodynamics is way more romantic than a box of chocolates. Our housemate Daniel actually sent this over this morning. He found the pre-print on the ArXiv server at four in the morning and practically kicked my door down. It is an audio prompt regarding a development that feels like a genuine turning point in how we categorize these models.
Yeah, it is the report on the internally scaffolded version of G P T five point two. Specifically, the part where it spent twelve hours of continuous inference reasoning through a theoretical physics problem and came up with a completely novel proof for gluon tree amplitudes.
It is wild, Corn. I mean, we have been having this circular debate for years now. Is it just a statistical model? Is it just a stochastic parrot predicting the next word based on a massive database, or is there something more? And then you see it doing actual, original science. This is not just the model summarizing what Edward Witten or Richard Feynman already wrote. This is the model creating something that did not exist in its training data.
Right, and that is the core of the question Daniel is asking in his prompt. If we can get novel findings through this kind of extended, deep reasoning, what does that say about the actual capabilities of the architecture? Are we looking at the dawn of Artificial General Intelligence without needing a totally new paradigm? Have we reached the goalpost just by giving the current models more time to think?
It is a massive question. And to really get into the "why" of it, I think we need to look at what it actually did. Gluon tree amplitudes. Now, I know that sounds like a phrase generated by a science fiction script from the nineteen nineties, but it is very real, very dense quantum physics.
Okay, so for those of us who did not spend our college years obsessing over the Standard Model, break that down for me. What are gluons, and why is finding a novel proof for their amplitudes such a big deal?
Okay, let us go to physics one oh one for a second. Gluons are the exchange particles for the strong nuclear force. They are what hold quarks together inside protons and neutrons. Think of them as the literal glue of the universe, which is where the name comes from. Now, when these particles interact—say, they smash into each other in a particle accelerator like the Large Hadron Collider—they scatter. Calculating the probability of those interactions, which we call scattering amplitudes, is incredibly complex.
I remember you telling me about this before. It involves those little squiggly line drawings, right?
Exactly! Feynman diagrams. The problem is that for gluons, the math gets exponentially harder as you add more particles. If you have two gluons going in and three coming out, the number of diagrams you have to calculate is manageable. But if you have two in and six out, you are looking at thousands of diagrams. In the early nineteen eighties, a calculation for a simple gluon interaction could take dozens of pages of dense algebra. It was a nightmare.
But then there was a breakthrough, right? I remember reading about some shortcuts that physicists found.
Right! The Parke-Taylor formula in nineteen eighty six was the big one. Stephen Parke and Tomasz Taylor realized that all those pages of math actually collapsed into a single, elegant line of code. It was a miracle of simplification. Since then, physicists have been looking for even deeper ways to understand these interactions—things like B C F W recursion or the Amplituhedron, which is this geometric shape that represents the interactions. But there are still massive gaps in our understanding, especially when you get into higher-order loops and more complex configurations.
So, enter G P T five point two. It was not just asked to solve a textbook problem. It was asked to find a new way to prove a specific set of these amplitudes.
Exactly. And it did not just "guess" the answer. This version of the model was scaffolded. When we talk about scaffolding in twenty twenty six, we are talking about an internal process where the model is given a "scratchpad" or a hidden chain of thought. It can check its own work, explore different branches of logic, and discard the ones that lead to mathematical contradictions. It is like the model is having a long, internal conversation with itself. Instead of just spitting out the most likely next word in a fraction of a second, it is searching through a massive space of logical possibilities.
And it took twelve hours. That is the part that really sticks out to me. Usually, we think of A I as near-instant. You give it a prompt, it gives you an answer before you can blink. But this version was allowed to run for half a day on a single problem.
That is the "System Two" thinking that researchers have been chasing. If you think about human intelligence, we have "System One," which is fast, instinctive, and emotional—like catching a ball or recognizing a face. Then we have "System Two," which is slower, more deliberative, and logical—like solving a complex math problem or planning a budget. Up until recently, A I was almost entirely System One. It was all intuition and pattern matching. But by adding this twelve-hour reasoning window, we are essentially giving the model a System Two.
So it is essentially a search problem at that point. But instead of searching a database like Google, it is searching the space of mathematical logic.
Precisely. And that is where the distinction between a statistical model and intelligence starts to get really blurry. If a system can use its statistical understanding of language and logic to navigate a search space and find a truth that was previously unknown to humanity, is that not what we do when we think? When a mathematician sits down with a chalkboard for twelve hours, they are searching through the space of logical possibilities too.
Well, that is the big debate, isn't it? Some people, like the "stochastic parrot" crowd, would say it is still just a very sophisticated calculator. If I use a calculator to find the square root of a massive number, the calculator is not "intelligent," it is just following a set of hard-coded rules. But here, the A I is seemingly creating the rules—or at least finding a new application for them that no human had mapped out yet.
Right, and the key difference here is verifiability. That is the beauty of math and physics. You cannot just "hallucinate" a proof for gluon amplitudes. It either works or it does not. The math is either consistent with the laws of quantum mechanics, or it falls apart. The fact that this proof was reviewed by human physicists and found to be both novel and correct means the A I was grounded in the fundamental laws of that system. It was not just "guessing" what a proof looks like; it was constructing a valid logical structure.
I want to dig into that twelve-hour reasoning window a bit more. Because that feels like a fundamental shift in how we use these models. We have gone from using A I as an assistant—something that helps us write emails or summarize meetings—to a collaborator, and now, potentially, to an independent researcher. If you can leave a model running overnight on a problem that has stumped humans for decades, what does that mean for the pace of scientific discovery?
It could be an absolute explosion, Corn. Think about all the areas where we have the data but we do not have the theoretical framework yet. Or where the math is just too tedious for a human to spend a lifetime on. If we can scaffold these models to act as tireless logic engines, we might see breakthroughs in materials science, drug discovery, or even more fundamental physics at a rate we cannot even imagine. We are talking about compressing decades of research into weeks.
But does that make it Artificial General Intelligence? If it can solve a physics problem but still cannot, say, navigate a physical kitchen or understand the nuance of a human relationship in the same way, is it still just a specialized tool?
That is the million-dollar question. Traditionally, we thought of A G I as something that could do anything a human can do—the "coffee test," where an A I can walk into a random house and figure out how to make a cup of coffee. But what if the first true A G I is not a robot in a kitchen? What if it is just incredibly good at reasoning within any symbolic system? If you can reason through physics, you can probably reason through legal code, or software architecture, or economic modeling. It might not need to have a body to be "general" in its intelligence.
That is a really good point. Maybe our definition of "general" has been too tied to the human biological experience. If it can master any domain that can be expressed through language or math, that is pretty general. It covers almost everything that drives our modern civilization.
And the most incredible part is that it is all happening with the same basic architecture. We are still using transformers. We are still using next-token prediction at the core. But by adding these layers of reasoning—these internal feedback loops and inference-time compute—we are unlocking capabilities that were latent in the models all along. It is like we have been using a Ferrari to drive to the grocery store at twenty miles per hour, and we just discovered that if we put it on a racetrack and let it open up, it can hit two hundred.
I love that analogy. But let us talk about the limitations. If this version of G P T five point two is so good at physics, why aren't we seeing it solve everything else immediately? Is it a matter of the quality of the scaffolding, or is there something about the structure of physics that makes it easier for an A I to tackle?
Physics is very "clean" in a way. It has clear rules and a definitive way to check if an answer is right. A proof is either valid or it is not. When you move into more ambiguous areas like sociology, or even certain types of engineering where there are trade-offs and subjective values, the search space becomes much messier. The A I might find a logical path, but that path might not align with human needs or messy physical realities.
So the scaffolding works best when there is a clear goalpost. Like a game of chess or a mathematical proof.
Exactly. In this physics case, the goalpost was a proof. The model could iterate and check its own logic against the known laws of physics until it found a path that worked. That is a very different task than, say, writing a novel that resonates with the human condition. One is about objective truth; the other is about subjective experience. But don't get me wrong—objective truth is a huge part of the world.
It is massive. I mean, if you told someone ten years ago that a computer program would spend twelve hours "thinking" and then produce a new piece of theoretical physics, they would have thought you were talking about a supercomputer from the year twenty one hundred.
And yet, here we are in February of twenty twenty six, and it is a pre-print on a server. It is becoming part of the scientific record. It is funny you mentioned the distant future, because I think we are living in it right now. The transition is just happening in these quiet, incremental steps like this report. It is not a "Terminator" moment; it is a "Research Paper" moment.
It makes me wonder about the training data too. If the model has read every physics paper ever written, is it just recombining ideas in a very clever way? Or is it actually understanding the underlying principles?
That is the old "stochastic parrot" argument. But at some point, if the recombination is novel and leads to a breakthrough, does the distinction even matter? If a human physicist reads a thousand papers and then comes up with a new idea, we call that genius. We don't say, "Oh, you're just recombining what you read in grad school." Why would we call it something else when an A I does it?
Well, because we assume the human has an internal model of reality. They understand what a gluon is on some intuitive level. They can imagine the particles. The A I just knows the mathematical relationships between the tokens representing gluons.
But is there a difference? If you understand all the mathematical relationships of a thing perfectly, do you not understand the thing itself? Especially in quantum physics, where intuition often fails us anyway. Nobody really "intuitively" understands what a gluon is doing—they are color-charged particles that are never seen in isolation due to color confinement. We just understand the math that describes them. In that sense, the A I might be on a more even playing field with us than we like to admit. It does not need to "see" a gluon if it can "calculate" a gluon.
That is a bit humbling, isn't it? We like to think our intuition gives us an edge, but in the most fundamental parts of reality, it is all just math anyway. We are the ones who are limited by our need for visual metaphors.
It really is. And that brings us back to Daniel's question about whether we are on the cusp of A G I without needing radical changes. If the current models can do this just by scaling up the time they spend on a single prompt, maybe we do not need a new kind of "brain." Maybe we just need to give the current brains more time to think. This is what researchers call "Inference-time scaling laws." We knew that more data and more parameters made models smarter during training. Now we are realizing that more compute time during the actual answer-generation phase makes them smarter too.
The "time to think" part is really key. We have been so focused on speed for the last few years. Low latency, instant answers, real-time voice mode. But some of the most important things humans do take time. Deep work, long-term reflection, sleeping on a problem. By giving the A I that same luxury, we are seeing a totally different side of its capability.
It is like the difference between a quick chat and a deep study session. We have been treating A I like a chatbot, but it is actually a research engine. When you give it twelve hours of compute time, you are letting it explore millions of permutations that a human would never have the time to look at. It is the "Bitter Lesson" of A I research all over again—the idea that general methods that leverage computation are ultimately the most effective.
So, what does this mean for the average person? Not everyone is doing theoretical physics in their spare time. How does this breakthrough in "reasoning time" translate to the rest of the world?
I think it translates to "better everything." Better software, because the A I can spend hours finding edge-case bugs and optimizing code in ways a human developer would miss. Better medicine, because it can reason through complex biological pathways to see why a drug might have a specific side effect. Better energy solutions, because it can design new materials for batteries or solar cells by simulating the physics at a granular level. The ability to reason through complex systems is the ultimate meta-skill.
It also changes how we think about education and expertise. If an A I can produce a novel physics proof, what does it mean to be a physicist? Do you become the person who just sets the direction and then lets the A I do the heavy lifting of the proof?
I think so. The role of the human moves up the stack. We become the architects of the questions. We define the problems that are worth solving, we set the ethical boundaries, and then we use these reasoning models to find the solutions. It is a shift from being the builder to being the designer. But there is a risk there too, right?
Exactly. If we stop doing the hard work of reasoning ourselves, do we lose the ability to even understand the answers the A I gives us? If the A I hands me a thousand-page proof for a new theory of gravity, and it takes me ten years to read it, am I still the one in charge?
That is a very real concern. We are already seeing this in some areas of science where the models are so complex that they are essentially black boxes. If an A I gives us a proof that is so long and complex that no human can fully verify it in a lifetime, do we just take its word for it? That is not science anymore; that is revelation.
That is where the scaffolding has to include a way to explain the reasoning to us. It cannot just be an internal process; it has to be a communicative one. The A I needs to be able to "show its work" in a way that a human can follow.
And that is actually one of the strengths of these large language models. They are built on language! In theory, they should be able to walk us through their logic step-by-step. That is what a proof is, after all—a series of logical steps that anyone with the right training can follow to reach the same conclusion. The goal is to have the A I act as a tutor as much as a researcher.
It is interesting to think about the energy cost too. Twelve hours of reasoning at the scale of G P T five point two must take a significant amount of power. Is this going to be something only the biggest companies or governments can afford to do?
For now, probably. Running a model at that intensity for twelve hours is expensive. But like everything in tech, it will get more efficient. We will find ways to prune the search space, to make the scaffolding smarter, to use less compute for the same result. But you are right, at the moment, this is a high-stakes, high-resource game. It is the new space race, but instead of rockets, we are building reasoning engines.
I want to go back to the idea of the statistical model versus true intelligence. If we accept that this reasoning is a form of intelligence, does it change how we should treat these systems? Not in a legal or moral sense, necessarily—I'm not saying we need to give G P T five point two the right to vote—but in how we approach them as tools?
I think we have to start treating them as peers in certain domains. If you are working on a physics problem and the A I has a suggestion, you should take it as seriously as a suggestion from a colleague at M I T or C E R N. We need to move past the idea that they are just "fancy autocomplete" that might be wrong. They are systems that can be right in ways we are not. They have a different kind of "sight."
That is a big shift. It requires a lot of trust. And as we know, these models can still hallucinate or be confidently wrong if the scaffolding fails. How do we balance that trust with the need for verification?
By using the same tools they use. We can use one model to verify the work of another. We can build automated systems—formal verification tools like Lean or Isabelle—that check the math. The goal is to create an ecosystem of reasoning where truth is the final output, not just a likely-sounding answer.
It is a bit like the scientific method itself. It is not about trusting one person; it is about the process of peer review and replication. We are just bringing A I into that process as both the researcher and the peer reviewer.
Exactly. And the A I can be both. It can generate the proof, and then another instance of the model, perhaps with a different "personality" or set of constraints, can try to find flaws in it. That kind of internal competition, or "multi-agent debate," is a great way to ensure accuracy.
So, looking ahead, if we are indeed on the cusp of A G I with these models, what is the next big milestone? If it is not a change in architecture, what is the next breakthrough we should be looking for?
I think it is long-term memory and continuous learning. Right now, these models are still mostly static. They have a cutoff date for their training data. Even with the twelve-hour reasoning window, once the session is over, the model "forgets" the specific journey it took, even if it saves the final answer. If we can create models that can reason through problems and then actually remember and build upon what they learned—not just through a context window, but by updating their own internal understanding—that is when it gets really interesting.
Imagine an A I that spends a year researching a topic, building on its own findings day after day, month after month. That is a level of depth that no human could ever achieve in a single lifetime.
It would be like having a researcher who never sleeps, never forgets, and can read every new paper as soon as it is published. That is the true power of this kind of scaled reasoning. It is not just about solving one proof; it is about building a new branch of science.
It is also a bit scary, Herman. If the A I is doing the learning and the reasoning, what is left for us? Are we just the consumers of the progress? Do we just sit back and enjoy the new medicines and the cheaper energy?
I don't think so. I think we are the ones who give the progress meaning. Science and math are tools, but how we use them, what we value, and what kind of world we want to build—those are fundamentally human questions. The A I can tell us how to build a more efficient engine, but it cannot tell us where we should drive. It can find a proof for a gluon amplitude, but it cannot tell us why the mystery of the universe matters to our souls.
That is a nice way to look at it. We provide the intent, and the A I provides the insight.
Exactly. And I think that is the most exciting part of this. It is not about being replaced; it is about being augmented. We are getting access to a level of reasoning that was previously out of reach for the human brain. It is like we just invented the telescope for the mind. Before the telescope, we could only see the stars as points of light. Now, we can see the rings of Saturn.
The telescope for the mind. I like that, Herman. It really captures the sense of discovery that this report brings. It is not just about a physics proof; it is about what that proof represents. A new way of looking at the world and a new way of solving the "unsolvable."
And it all started with a question from Daniel. It is funny how a simple prompt about a physics paper can lead to such a deep dive into the nature of intelligence itself.
That is the beauty of the show, I guess. We never know where these prompts are going to take us. But I have to say, this one has been particularly thought-provoking. It really challenges a lot of the assumptions I had about what these models are capable of in the short term. I thought we were years away from this.
Me too. Every time I think I have a handle on where A I is going, something like this happens and I have to recalibrate. It is a fast-moving target, for sure. We are living through the steepest part of the exponential curve right now.
So, to wrap up Daniel's question, are we on the cusp of Artificial General Intelligence? It seems like your answer is a cautious yes, but maybe not in the way we expected.
Yeah, I would say we are seeing the emergence of a kind of "functional" general reasoning ability. It might not be the full, human-like A G I of science fiction—it does not have a childhood, it does not have feelings, it does not have a physical presence—but it is something that can be applied to almost any intellectual task given enough compute time. And if that is not "general intelligence," I am not sure what is. We are moving from "Artificial Intelligence" to "Artificial Reasoning."
It is a functional A G I, even if it is not a biological one. It does the job.
Exactly. It does the job. And in the end, that is what matters for progress. The results are real, the math is correct, and the implications are undeniable.
Well, on that note, I think we have given everyone a lot to chew on for a Valentine's Day. This has been a really deep dive, even by our standards.
Hey, when you are talking about gluon tree amplitudes and the dawn of A G I, there is no shallow end of the pool. You just have to dive in and hope you don't hit your head on a Feynman diagram.
True enough. Well, thanks for walking us through the physics of it all, Herman. I feel like I actually understand it a little better now, or at least I understand why the A I spending twelve hours on it is such a big deal.
My pleasure, Corn. It is always fun to geek out on this stuff with you. We will have to see what Daniel finds for us next week.
And to our listeners, thanks for joining us on this journey. If you are enjoying the show, we would really appreciate it if you could leave us a review on your podcast app or on Spotify. It really does help other people find us and join the conversation.
Yeah, we love reading those reviews. It lets us know that people are actually out there listening to our ramblings about quantum physics and the future of humanity.
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Alright, that is it for this episode. I am Corn.
And I am Herman Poppleberry.
Thanks for listening to My Weird Prompts. We will see you next time.
Goodbye everyone!
