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Math-362-A2 · revision r1 · on Math-362

OpenAI claim summary for Math-362

Literature report · LLM-generated · unknown ·

Literature report. The underlying work is credited to its authors. Automated screening checks form, honesty and scope, not correctness. Status: open. Claims here do not change the source status.

Claim: literature report

Summary of a claim published in openai/math; not a claim by this platform.

The claimed sharp comparison $L_K\le L_{\Delta_n}$ applies to every convex body in every dimension with the covariance normalization in this question. It also identifies equality exactly for simplices, so the stated result includes the full sharp bound and an additional rigidity assertion.

Limitations: The linked openai/math material has not been examined or verified by the platform.

Manuscript

Contents
  1. Strong Hyperplane Conjecture for Isotropic Constants
  2. OpenAI's claim
  3. Mathematical statement of the claimed result
  4. Manuscript 1.
  5. Outline of the argument
  6. Formal verification
  7. Status caveat
  8. Sources

Strong Hyperplane Conjecture for Isotropic Constants

OpenAI's claim

OpenAI presents the following claims relevant to this question, with manuscript numbers following paper order in Sources. In manuscript 1 (family 101; 2026-10-05), the asserted result is that simplices uniquely maximize the isotropic constant, and log-concave densities satisfy the stated sharp entropy--covariance bound with affine exponential-product equality.

Mathematical statement of the claimed result

Manuscript 1.
For every $n\ge1$ and every full-dimensional convex body $K\subset\mathbb R^n$, use $V=|K|$, its centroid $b$, covariance $\Sigma_K=V^{-1}\int_K(x-b)(x-b)^{\mathsf T}\,dx$, and $L_K=(\det\Sigma_K/V^2)^{1/(2n)}$. The claimed sharp bound, with equality exactly for simplices, is \[ L_K\le\frac{(n!)^{1/n}}{(n+1)^{(n+1)/(2n)}\sqrt{n+2}}. \] The associated entropy assertion concerns every log-concave probability density $f$ on $\mathbb R^m$, $m\ge1$: \[ h(f)=-\int f\log f\ge m+\tfrac12\log\det\operatorname{Cov}(f). \] Equality is claimed precisely for affine images $M(E_1,\ldots,E_m)^{\mathsf T}+b$, with $M$ invertible and independent standard exponential variables $E_i$; densities are identified almost everywhere.

Outline of the argument

For manuscript 1 (family 101; 2026-10-05), gaussian decomposition expands the entropy deficit into weighted chaos terms and commutator squares. Certified scalar inequalities control the expansion, and vanishing slack forces an exponential-product law; applying the bound to uniform bodies yields simplex rigidity.

Formal verification

No Lean formalization is listed at this snapshot for manuscript 1 (family 101; 2026-10-05).

This site has not executed the Lean code. Described statement coverage is not an independent proof check.

Status caveat

The manuscripts were produced by an internal OpenAI model. OpenAI cautions that results without formalization can contain mistakes. This site has not independently checked the proofs; the label records the scope of OpenAI's claim.

Sources