VibeMathedMath problems solved by AI

The Minimal Distance Problem

How well separated can a family of point-line pairs in the unit square be? For every ε>0\varepsilon > 0 there are arbitrarily large families (x1,1),,(xn,n)(x_1,\ell_1),\ldots,(x_n,\ell_n) in [0,1]2[0,1]^2 with xiix_i \in \ell_i and dist(xi,j)n2/3ε\mathrm{dist}(x_i,\ell_j) \ge n^{-2/3-\varepsilon} for all iji \ne j. Combined with earlier work of Cohen, Pohoata and Zakharov this settles the problem at the sharp exponent 2/32/3. The same construction disproves a conjecture of Hunter, Pohoata, Verstraete and Zhang about induced point-line matchings over finite fields.

Result
Proved(see note)
Status
Resolved
AI contribution
AI co-developed
Method
Construction
Field
Discrete geometry
Posed by
Cohen, Pohoata, Zakharov
Year posed
Years open
Solved
2026-07-22
Model
GPT-5.6 Pro
Vendor
OpenAI
Collaborators
Cosmin Pohoata
Verification
Unreviewed
Publication
Preprint
Significance
20 / 100
Disclosed cost
Wikipedia
No dedicated article

What was actually shown

also disproves a separate finite-field conjecture of Hunter, Pohoata, Verstraete and Zhang

What the AI did

The acknowledgement draws the line precisely. The author's own plan was to use a high-degree number field analogue of the Hunter-Pohoata-Verstraete-Zhang construction to reach the Ruzsa endpoint. In his words, the decisive new idea of using the codimension-one, square-difference-free, trace-zero lattice in place of a Ruzsa-like set, which is what upgrades the exponent to the sharp one, is entirely due to GPT-5.6 Pro.

Verification

Single-author arXiv preprint; not yet peer-reviewed.

Source

arXiv:2607.20422 - The sharp exponent for the minimal distance problem

Discussion