A team of physicists from various universities announced a breakthrough in theoretical physics on February 13, collaborating with OpenAI’s artificial intelligence model, GPT-5.2. While the precise findings of their research remain esoteric, the methods employed to reach this new result have drawn significant attention from the scientific community.
The study revolves around the analysis of particle collisions, specifically in the realm of particle physics. Physicists traditionally calculate the outcomes of such collisions using a method that involves drawing various Feynman diagrams, which represent all possible interactions among particles. The focus of this research was on the simplest type of diagram, known as tree diagrams, which depict linear interactions without any loops.
As the number of particles involved in a collision increases, the complexity of the calculations escalates dramatically. For collisions involving a small number of particles, physicists might need to evaluate thousands or even millions of tree diagrams, making the task exceedingly labor-intensive. Surprisingly, after all this effort, the final results often reveal a striking simplicity, leading physicists to question whether there might be more efficient methods of calculation.
This latest research specifically examined particle collisions involving gluons, which are fundamental particles that bind quarks together inside protons and neutrons. Gluons exhibit a characteristic called helicity, which can be likened to the orientation of a spinning football. In calculations of scattering amplitudes, it is crucial to track the helicity states of the gluons involved.
Historically, physicists believed that certain combinations of helicity states, particularly those with one gluon spinning in one direction amidst others spinning oppositely, were impossible. This research, however, challenged that notion, demonstrating that such configurations could exist under specific conditions known as a half-collinear configuration, where all particles are nearly aligned in the same direction. This revelation led to the formulation of a simple yet elegant equation for these previously disregarded tree-level amplitudes.
According to the study’s authors, the initial suggestion for this formula came from GPT-5.2 Pro, while a different internal AI model developed by OpenAI substantiated its validity. The human physicists involved then meticulously verified the formula against established mathematical principles to ensure its accuracy. They were able to derive explicit equations for three, four, and five gluons; however, by the time they reached six gluons, the traditional approach yielded a staggering 32 separate terms, highlighting the efficiency of the new formula, which is expressed in terms of n – 2 factors, where n is the number of particles.
“It happens frequently in this part of physics that expressions for some physical observables calculated using textbook methods look terribly complicated, but turn out to be very simple,” remarked Nima Arkani-Hamed, a professor at the Institute for Advanced Study. He further noted that simpler equations often pave the way for deeper insights and new theoretical frameworks.
While the collaboration between human physicists and AI has produced promising results, it also brings forth questions about the reliability of AI in theoretical physics. Previous projects have highlighted instances where AI-generated research, although innovative, has led to miscalculations or conceptual errors. In a paper released in January 2026, Stephen Hsu, a theoretical physicist, discussed how large language models (LLMs) like GPT-5 could assist in generating ideas and calculations but also noted their propensity to produce plausible-sounding errors.
In defense of AI’s potential, Hsu characterized research involving these models as akin to collaborating with a brilliant but erratic human researcher, capable of profound insights yet prone to significant mistakes. This view was echoed by other physicists, including Nirmalya Kajuri, who critiqued Hsu’s findings as being based on outdated approaches.
In a separate experiment, Jonathan Oppenheim utilized AI from Anthropic to tackle a complex calculation. While the AI managed to arrive at a solution faster than a student could, it initially produced an incorrect answer before correcting itself through iterative checks, illustrating both the potential and pitfalls of AI in scientific research.
As AI technology continues to evolve, its integration into scientific inquiry appears both promising and fraught with challenges. Experts suggest that while AI can significantly aid researchers, particularly in computational tasks, it may still fall short of independently producing accurate and innovative scientific work. This duality raises important questions about the future role of AI in theoretical physics and the scientific community at large, serving as both a tool and a subject for ongoing scrutiny.
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