GPT-5.6 closed a 30-year gap in optimization — with a 10-page prompt
A post from r/math hit #2 on Hacker News at 459 points: GPT-5.6 reportedly produced a proof closing a gap that had stood open for about 30 years in convex optimization. The claim, per commenters who work in the area: it proved that optimizing convex Lipschitz functions requires Omega(d^2) function evaluations — a lower bound that matches the upper bound of a 30-year-old algorithm. Which means the old algorithm was optimal all along, and the complexity of that problem class is now settled.
Caveats first, because they matter: this came from a reddit post, there's no peer-reviewed paper yet, and the proof is still being checked in public. But domain experts in the thread call it a real contribution rather than hype, and it lands in what has become a steady drumbeat — the Erdos unit-distance disproof, the CDC proof two weeks ago, Terence Tao openly vibe-coding his research. Frontier models producing genuinely new mathematics is no longer a one-off event. It's a genre.
The detail worth staring at: it took a 10-page prompt. Someone who deeply understood the problem had to specify it, frame it, and constrain it before the model could close the gap. The division of labor isn't disappearing, it's shifting — the human contribution moved from finding the proof to specifying the problem precisely enough that a machine can. That's the harness thesis, applied to mathematics: the scarce skill is writing the spec, and the 100X comes from knowing exactly what to ask for.
Discussion: news.ycombinator.com/item?id=48957779
← Back to all articles
Caveats first, because they matter: this came from a reddit post, there's no peer-reviewed paper yet, and the proof is still being checked in public. But domain experts in the thread call it a real contribution rather than hype, and it lands in what has become a steady drumbeat — the Erdos unit-distance disproof, the CDC proof two weeks ago, Terence Tao openly vibe-coding his research. Frontier models producing genuinely new mathematics is no longer a one-off event. It's a genre.
The detail worth staring at: it took a 10-page prompt. Someone who deeply understood the problem had to specify it, frame it, and constrain it before the model could close the gap. The division of labor isn't disappearing, it's shifting — the human contribution moved from finding the proof to specifying the problem precisely enough that a machine can. That's the harness thesis, applied to mathematics: the scarce skill is writing the spec, and the 100X comes from knowing exactly what to ask for.
Discussion: news.ycombinator.com/item?id=48957779
Comments