GPT-5.2 Spent 12 Hours Proving Gluons Do Something 'Impossible'

I was debugging a particularly nasty race condition last week when my physicist friend texted me: "Dude, GPT just became a co-author on a physics paper." Not helped with research. Not assisted humans. Co-authored. Like, its name is literally on the preprint alongside researchers from Harvard, Cambridge, and Princeton.

Turns out GPT-5.2 didn't just help—it discovered something physicists had written off as impossible for decades.

The Cosmic Impossibility That Wasn't

For years, theoretical physicists taught that certain gluon amplitudes (think probability measures for particle interactions) always equal zero. Always. This wasn't some fringe theory—it was taught with the same certainty as Newton's laws. A "cosmic impossibility," as some called it.

GPT-5.2 looked at this assumption and basically said: "Hold my training data."

The AI discovered that the zero-amplitude conclusion only holds when particles move in ordinary trajectories. But in a specific scenario called the half-collinear regime—where gluon momenta align in a precise, non-typical way—the amplitude doesn't vanish. It proposed Equation 39, a closed-form formula describing exactly how these interactions behave.

Twelve Hours of Robot Physics

Here's where it gets wild. The research process looked like this:

• Human physicists calculated amplitudes for small numbers of gluons by hand (producing nightmarishly complex expressions with dozens of terms)
• GPT-5.2 Pro simplified these expressions and spotted a pattern
• A specialized, scaffolded version of GPT-5.2 then spent 12 hours reasoning autonomously through the problem
• It arrived at the same formula and produced a formal mathematical proof

Twelve. Hours. Of. Autonomous. Mathematical. Reasoning.

They verified the result using the Berends-Giele recursion method, confirmed it satisfied the soft theorem, and ran five strict consistency tests. It all checked out.

This Changes the Conversation

We've moved past "Can AI actually think?" to "How fast will AI rewrite what we thought we already knew?"

The methodology here is fascinating and replicable:

1. Use AI to simplify human-derived expressions

2. Leverage pattern recognition for general formulas

3. Deploy specialized reasoning versions for autonomous proof generation

4. Implement rigorous human verification

5. Apply multiple consistency checks

But let's be real about limitations. This applies specifically to tree-level amplitudes in special kinematic regimes. Loop corrections with quantum fluctuations? Still brutally complex. The half-collinear configuration isn't generic in ordinary spacetime—it's a special momentum alignment that exists more in mathematical space than your kitchen table.

The Graviton Gold Rush

OpenAI isn't stopping here. They're already extending these methods to study gravitons—hypothetical particles that carry gravity. If they crack that nut, we're talking about progress on one of physics' biggest unsolved problems: unifying quantum mechanics and gravity.

Kevin Weil from OpenAI is co-credited on the preprint, signaling this isn't a one-off publicity stunt. This is strategic positioning for the scientific research market. Academic institutions are watching.

My Bet: Within 18 months, we'll see AI co-authors on papers across multiple scientific disciplines, not just physics. The 12-hour autonomous reasoning capability is the real breakthrough here—it suggests LLMs can handle extended mathematical problems that require sustained logical chains. That's applicable far beyond gluons. But the key insight from Harvard's Strominger is crucial: human guidance remains essential. This isn't AI replacing physicists; it's AI as an incredibly sophisticated research partner that can explore mathematical paths humans would never attempt.