OpenAI claim summary for Math-2482
mathematicsTop Open Problemscontributionliterature_reportClaim: literature report
Summary of a claim published in openai/math; not a claim by this platform.
Critical Bernoulli dimension: The theorem determines the one-homogeneous minimizing critical dimension exactly as seven, proving flatness through dimension six and a nonflat seven-dimensional example. The catalogue bundles this with singular-set, two-dimensional branch-point symmetry removal and frequency-method questions, so determining d* does not resolve the complete record.
This page records partial support. Complete resolution of the catalogue question is not established by this record.
Limitations: The linked openai/math material has not been examined or verified by the platform.
Manuscript
Contents
Singular Free Boundaries in the Critical Dimension
OpenAI's claim
Critical Bernoulli dimension: The claimed first nonflat minimizing Bernoulli cone occurs in dimension seven; all homogeneous global solutions that minimize in lower dimensions are flat. Dimensions at most six have smooth local free boundaries, with the announced sharp singular-set bounds in higher dimensions.Mathematical statement of the claimed result
Mathematical claim 1.
For nonnegative $u\in H^1_{\mathrm{loc}}(\mathbb R^d)$, call it a global one-phase minimizer if on every ball it minimizes \[J(u;B)=\int_B(|\nabla u|^2+\mathbf1_{\{u>0\}})\,dx\] against all nonnegative Sobolev competitors with the same boundary trace. The claim states that every nonzero one-homogeneous global minimizer, $u(rx)=ru(x)$, in $1\le d\le6$ is $(x\cdot e)_+$ with $|e|=1$, and some nonflat one-homogeneous global minimizer exists in $\mathbb R^7$. Thus the least nonflat minimizing-cone dimension is $d_*=7$. This concerns global minimizers rather than all stable critical points.Outline of the argument
Critical Bernoulli dimension: A first hypothetical nonflat minimizer below dimension seven is reduced to a spherical component in dimension six. Tensor identities and stability produce a flux comparison whose algebraic estimate is verified with exact coefficient data. Removing cutoffs contradicts the spherical configuration; the known nonflat seven-dimensional construction gives the other side and the sharpness consequences.Formal verification
Family 367 describes Lean coverage for existence of a nonflat homogeneous minimizer in dimension seven; the lower-dimensional flatness and singular-set classifications are separate.Critical Bernoulli dimension: The manuscript links BernoulliNonflatInSeven.lean.
The documented selection does not cover the entire manuscript assertion relevant here.
This site has not run the Lean development. These links describe OpenAI’s selected formal statements and should be read with their stated scope.
Status caveat
OpenAI reports that an internal model produced the manuscripts. Their arguments have not been independently verified as proofs by this site. Any portion outside the documented formal scope may contain mistakes, and the catalogue label reports the announced mathematical claim.Sources
- OpenAI, The critical dimension for one-phase Bernoulli minimizers, 2026-09-24; citation key
OAI:The-critical-dimension-for-one-phase-Bernoulli-minimizers-September-24-2026. PDF (GitHub); PDF (direct); Manuscript page and citation; Latest version; Statement source: thm:main. - Family 367: Lean scope.
- Comparator BernoulliNonflatInSeven.json.
- Comparator BernoulliNonflatInSeven.lean.
- OpenAI catalogue entry.