In a moment that may define the 2026 academic calendar, theoretical physics has undergone a digital transformation. On Friday, February 13, a collaborative team from Harvard, Cambridge, the Institute for Advanced Study (IAS), and OpenAI released a landmark preprint titled “Single-minus gluon tree amplitudes are nonzero.”
For over forty years, the standard textbook argument stated that if you have a collection of gluons where only one has “negative helicity” (a specific spin orientation), the probability of them interacting—the scattering amplitude—is zero. GPT-5.2 Pro has just proved the world wrong.
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Quantum Leap: GPT-5.2 vs. The Strong Nuclear Force
The study focuses on gluons, the subatomic particles responsible for the strong nuclear force that holds quarks together.
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The Complexity Barrier: Calculating these interactions for many particles usually results in “superexponential” complexity. Human researchers had managed to work out expressions for up to six gluons by hand, but the resulting formulas were too messy to find a universal pattern.
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The AI Intervention: GPT-5.2 Pro was given these complex expressions. It didn’t just calculate them; it refactored the math, spotted a hidden pattern, and conjectured a simplified formula valid for any number of gluons ($n$).
The 12-Hour Reasoning Cycle: How the Formula was Born
According to OpenAI’s research blog, the model utilized a specialized “scaffolded” reasoning mode.
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Conjecture: The Pro model first posited the general formula after seeing the simplified human-calculated base cases.
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Deep Reasoning: An internal version of the model then entered a 12-hour “Deep Think” loop. It worked through the Berends-Giele recursion relation—a standard but grueling method for building multi-particle amplitudes.
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Verification: The model successfully produced a formal proof that the new formula satisfies the Weinberg Soft Theorem, which governs how particles behave at low energies.
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Half-Collinear Regime: The Special Conditions for Discovery
The “investigative” secret behind why this interaction was missed for so long lies in the Half-Collinear Regime.
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The Loophole: Traditional physics assumed “generic” momentum, where particles fly off in random directions.
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The Specific Alignment: The researchers (and GPT-5.2) identified that when gluon momenta follow a specific, precise alignment—known as half-collinear—the math no longer cancels out to zero.
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Beyond Gluons: The team has already confirmed that this logic extends to gravitons (the theoretical particles of gravity), potentially opening a new door to quantum gravity research.
Investigative Angle: Is this the End of the “Manual” Physicist?
While some critics on platforms like Hacker News argued this was merely “formula refactoring,” the academic community disagrees. Nathaniel Craig, a professor at UC Santa Barbara, noted that this is “journal-level research advancing the frontiers of theoretical physics.”
However, an investigative look at OpenAI’s recent “Prism” workspace initiatives suggests this is part of a larger strategy to move AI from a “chat assistant” to an “autonomous research agent.” By allowing models to run for hours on a single problem, OpenAI is effectively competing with Google’s Gemini 3 Deep Think, which recently solved 18 major research problems across biology and math.
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[THE PHYSICS BREAKTHROUGH: AT A GLANCE]
| Feature | Old Assumption | GPT-5.2 Discovery (2026) |
| Interaction Type | Single-minus Gluon Amplitude | Non-zero in Half-Collinear Regime |
| Methodology | Manual Feynman Diagrams | AI-Conjectured Pattern & Proof |
| Reasoning Time | Decades (Unsolved) | 12 Hours (Verified) |
| Extension | Limited to Yang-Mills | Generalizable to Gravitons |
Next Steps
If you are a student or researcher in high-energy physics, you should access the preprint on arXiv (2602.12176) to review the new closed-form expression. Furthermore, if you are a developer, you should monitor the upcoming OpenAI Science API updates expected in late February, which may allow researchers to deploy similar “scaffolded” reasoning tasks for other verifiable mathematical conjectures.
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