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Lean statement

Independent-product spaces and positive midpoint moduli

Lean is a language for mathematical statements and proofs that a computer can verify. This page shows one statement file from OpenAI’s release, possible paper matches and independent reports. The code-like names below identify the definitions or theorems selected by the proof-verification settings.

Theorems selected for verification: OAI.IndependentProducts.main_general OAI.IndependentProducts.main_exponential OAI.IndependentProducts.main_gaussian_24 OAI.IndependentProducts.main_gaussian_12

Lean statement file: IndependentProducts.leanShow on the mapFamily 331. OpenAI lists this as a main result in its Lean catalogue.

The Lean statement

The lines of the Lean statement that state the theorem, then the definitions in the file that it uses.

theorem main_general (μ : Measure ℝ) (hμ : MultiplierHypotheses μ) :    ¬ FiniteDimensional ℝ (productSpan μ) ∧    ∀ N : Seminorm ℝ (productSpan μ),      IsEquivalentNorm (productSpan μ) N → ¬ IsAUC (productSpan μ) N := by  sorrytheorem main_exponential (t : ℝ) (ht : 0 < t) :    (7 * t / 30) * excessConstant (10 / t) ≤      averagedMidpointModulus (productSpan exponentialLaw)        (normSeminorm ℝ (productSpan exponentialLaw)) t ∧    0 < (7 * t / 30) * excessConstant (10 / t) := by  sorrytheorem main_gaussian_24 (t : ℝ) (ht : 0 < t) :    gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 ≤      averagedMidpointModulus (productSpan gaussianSquareLaw)        (normSeminorm ℝ (productSpan gaussianSquareLaw)) t ∧    0 < gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 := by  sorrytheorem main_gaussian_12 (t : ℝ) (ht : 0 < t) :    gaussianTail (8 * Real.sqrt 6 / t) / 4 ≤      averagedMidpointModulus (productSpan gaussianSquareLaw)        (normSeminorm ℝ (productSpan gaussianSquareLaw)) t ∧    0 < gaussianTail (8 * Real.sqrt 6 / t) / 4 := by  sorry

A Lean statement states the theorem and leaves its proof as sorry on purpose. OpenAI’s proof is in a separate solution module, and a program called Comparator checks that proof against this statement.

Definitions needed to read it

Local definitions the statement names, followed through the definitions they name in turn. The match is by name in the source text, so a name reached only through notation can be missed; the full file is below.

  • abbreviation OAI.IndependentProducts.Vertex line 15

    abbrev Vertex := List ℕ+
  • abbreviation OAI.IndependentProducts.Edge line 17

    abbrev Edge := {v : Vertex // v ≠ []}
  • abbreviation OAI.IndependentProducts.Sample line 18

    abbrev Sample := Edge → ℝ
  • definition OAI.IndependentProducts.productMeasure line 21

    def productMeasure (μ : Measure ℝ) : Measure Sample :=  Measure.infinitePi (fun _ : Edge => μ)
  • definition OAI.IndependentProducts.pathPrefix line 25

    def pathPrefix (v : Vertex) (j : Fin v.length) : Edge :=  ⟨v.take (j.val + 1), by    intro h    have hl := congrArg List.length h    simp only [List.length_take, List.length_nil] at hl    have := j.isLt    omega⟩
  • definition OAI.IndependentProducts.pathProduct line 34

    def pathProduct (v : Vertex) (ω : Sample) : ℝ :=  ∏ j : Fin v.length, ω (pathPrefix v j)
  • definition OAI.IndependentProducts.productSpan line 38

    def productSpan (μ : Measure ℝ) : Submodule ℝ (Lp ℝ 1 (productMeasure μ)) :=  (Submodule.span ℝ {f : Lp ℝ 1 (productMeasure μ) | ∃ v : Vertex,    (f : Sample → ℝ) =ᵐ[productMeasure μ] pathProduct v}).topologicalClosure
  • structure OAI.IndependentProducts.MultiplierHypotheses line 43

    structure MultiplierHypotheses (μ : Measure ℝ) : Prop where  probability : IsProbabilityMeasure μ  positive : ∀ᵐ w ∂μ, 0 < w  nonconstant : ∀ c : ℝ, ¬ (∀ᵐ w ∂μ, w = c)  mean_one : ∫ w, w ∂μ = 1  second_moment : MemLp (fun w : ℝ => w) 2 μ
  • definition OAI.IndependentProducts.CofiniteSubspaces line 53

    def CofiniteSubspaces : Set (Submodule ℝ X) :=  {F | IsClosed (F : Set X) ∧ FiniteDimensional ℝ (X ⧸ F)}
  • definition OAI.IndependentProducts.averagedMidpointModulus line 57

    def averagedMidpointModulus (N : Seminorm ℝ X) (t : ℝ) : ℝ :=  sInf {r | ∃ x : X, N x = 1 ∧ r =    sSup {a | ∃ F ∈ CofiniteSubspaces X, a =      sInf {b | ∃ y : X, y ∈ F ∧ 1 ≤ N y ∧        b = (N (x + t • y) + N (x - t • y)) / 2 - 1}}}
  • definition OAI.IndependentProducts.oneSidedModulus line 64

    def oneSidedModulus (N : Seminorm ℝ X) (t : ℝ) : ℝ :=  sInf {r | ∃ x : X, N x = 1 ∧ r =    sSup {a | ∃ F ∈ CofiniteSubspaces X, a =      sInf {b | ∃ y : X, y ∈ F ∧ N y = 1 ∧ b = N (x + t • y) - 1}}}
  • definition OAI.IndependentProducts.IsEquivalentNorm line 70

    def IsEquivalentNorm (N : Seminorm ℝ X) : Prop :=  ∃ a b : ℝ, 0 < a ∧ a ≤ b ∧ ∀ x, a * ‖x‖ ≤ N x ∧ N x ≤ b * ‖x‖
  • definition OAI.IndependentProducts.IsAUC line 73

    def IsAUC (N : Seminorm ℝ X) : Prop := ∀ t : ℝ, 0 < t → 0 < oneSidedModulus X N t
  • definition OAI.IndependentProducts.gaussianTail line 76

    def gaussianTail (r : ℝ) : ℝ :=  (gaussianReal 0 1 {x : ℝ | r ≤ |x|}).toReal
  • definition OAI.IndependentProducts.excessConstant line 80

    def excessConstant (K : ℝ) : ℝ :=  (1 / 16 : ℝ) * ∫ x : ℝ, max (|x| - 2 * K) 0 ∂gaussianReal 0 1
  • definition OAI.IndependentProducts.exponentialLaw line 84

    def exponentialLaw : Measure ℝ := expMeasure 1
  • definition OAI.IndependentProducts.gaussianSquareLaw line 87

    def gaussianSquareLaw : Measure ℝ := (gaussianReal 0 1).map (fun x : ℝ => x ^ 2)

Possible paper match, with no recorded review

Atlas selected this possible match by comparing words in the Lean statement with every statement in the family. No review of whether the statements mean the same thing is recorded.

Words of the Lean statement, its names split into words, that also appear in the theorem: 7t/30, inherited, exponential, multiplier, square, gaussian, laws, exp and six more. Found in the paper’s title, which its main theorem states: independent, products, asymptotic, midpoint and real. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: space.

Statements in Independent products in real L1: asymptotic midpoint convexity without AUC renormings come first because the release’s name for this Lean statement shares words with that paper’s title.

Theorem 1.1 of Independent products in real L1: asymptotic midpoint convexity without AUC renormings

Theorem

For every multiplier W as above, the Banach space X_W is infinite dimensional and has no equivalent AUC norm. For the exponential and Gaussian-square laws, the inherited norms satisfy

\begin{aligned}\whdelta_{X_{\mathrm{exp}}}(t) &\ge \frac{7t}{30}c(10/t)>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge4\sqrt2(2+\sqrt2)/t\bigr)}{8}>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge8\sqrt6/t\bigr)}{4}>0 &&(t>0).\end{aligned}
Section 1, Introduction Read it in the paper TeX source, line 57
Show the TeX
For every multiplier $W$ as above, the Banach space $X_W$ is infinite
dimensional and has no equivalent AUC norm.
For the exponential and Gaussian-square laws, the inherited norms satisfy
\begin{align}
 \whdelta_{X_{\mathrm{exp}}}(t)
 &\ge \frac{7t}{30}c(10/t)>0 &&(t>0),\\
 \whdelta_{X_{\mathrm{sq}}}(t)
 &\ge\frac{\PP\bigl(|G|\ge4\sqrt2(2+\sqrt2)/t\bigr)}{8}>0
 &&(t>0),\\
 \whdelta_{X_{\mathrm{sq}}}(t)
 &\ge\frac{\PP\bigl(|G|\ge8\sqrt6/t\bigr)}{4}>0
 &&(t>0).
\end{align}

Other possible paper matches, closest wording first

  • Theorem 1.1 of Exact asymptotic moduli in a Daugavet subspace of L1. Words of the Lean statement, its names split into words, that also appear in the theorem: inherited, probability, omega, measure, auc, dimensional and eight more. Found in the paper’s title, which its main theorem states: asymptotic. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: space.
  • Corollary 17.2 of Midpoint lenses in segment spaces. Words of the Lean statement, its names split into words, that also appear in the theorem: cofinite, direction, averaged, least, omega, modulus and five more.
  • Theorem 1.1 of Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space. Words of the Lean statement, its names split into words, that also appear in the theorem: least, integer, auc, dimensional, equivalent, infinite and two more. Found in the paper’s title, which its main theorem states: tree, asymptotic and midpoint. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: space.
  • Proposition 9.1 of Midpoint lenses in segment spaces. Words of the Lean statement, its names split into words, that also appear in the theorem: sided, vertex, path, modulus, root, auc and eight more.
  • Proposition 3.1 of Independent products in real L1: asymptotic midpoint convexity without AUC renormings. Words of the Lean statement, its names split into words, that also appear in the theorem: exponential, product and exp. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: space.
  • Corollary 7.17 of Midpoint convexity from bounded tree potentials and path costs. Words of the Lean statement, its names split into words, that also appear in the theorem: independent, modulus, positive, subspace, asymptotic, midpoint and three more.

How Atlas selects possible matches: it compares words in the Lean statement with every statement in the family. It also splits Lean names into words. The release’s label and note receive less weight; a main theorem wins a close match. If the Lean wording yields no clear match, Atlas uses the catalogue description. This selects passages to compare; it does not establish that they mean the same thing.

Reported Comparator runs

three independent groups report that Comparator accepted runs for this Lean statement file: Carlos Toledo’s cert-machine; MathVet; Neal McBurnett’s openai-math-lean-tooling. Each group reports its own Comparator run for this statement file. Atlas has not repeated the runs.

For a configured theorem, Comparator tests whether OpenAI’s proof proves the exact Lean statement. Some files instead configure definitions for comparison by name and type. Acceptance does not establish that the statement means the same thing as the paper’s theorem.

Carlos Toledo’s cert-machine

How they ran it, in their words

Every Comparator challenge re-run on Linux with nanoda switched on, decided by a preregistered rule; every family’s challenge statements read against OpenAI’s headline, clause by clause. Decider: Comparator (leanprover/comparator d03acab1 = tag v4.34.0, built with the release’s toolchain v4.34.1), lean4export 076e8e57, landrun 811cfff5, nanoda_lib 3a240721 — on GitHub’s Linux runners (.github/workflows/openai-math-kernel.yml), one j…

  • Accepted Their term: “CERTIFIED”.

    Re-run through Comparator with the independent nanoda kernel switched on: Lean’s kernel and nanoda both accept OpenAI’s solution.

    Reported 10 October 2026, at the release version shown here. Original verification report

MathVet

How they ran it, in their words

An independent, model-assisted, pre-referee reading of each family’s challenge statements against its headline claim under a published rubric (PROTOCOL.md), with its own Comparator runs per challenge. Its protocol describes a planned error-rate study; this import does not establish that the study has been completed.

  • Accepted Their term: “comparator-sandboxed-pass”.

    Re-run through Comparator in a sandboxed virtual machine: accepted.

    Reported 8 October 2026, at the release version shown here. Original verification report

Neal McBurnett’s openai-math-lean-tooling

How they ran it, in their words

Each challenge’s solution is built and checked by Comparator at the pinned release, on Lean leanprover/lean4:v4.34.1, recording the verdict, the check time and the peak memory; the run was still in progress at this snapshot (192 accepted, 224 pending).

  • Accepted Their term: “accepted”.

    Re-run through Comparator with Lean’s kernel: accepted (the check took 235 s; peak memory 6.4 GiB).

    Reported 10 October 2026, at the release version shown here. Original verification report

What an accepted Comparator run would establish

If Comparator is run under this configuration and accepts, the solution proves the configured theorem using only the allowed axioms, and the Lean kernel accepts every step.

This configuration lists no definition holes, so every definition the statement uses is compared as it is written in this file.

Allowed axioms: propext, Quot.sound, Classical.choice

Acceptance leaves open whether the Lean statement means the same thing as the paper’s theorem, whether the paper’s other claims are correct, and whether the result is new. Those questions require comparing the definitions and statements, reviewing the paper’s arguments, and reading the earlier literature.

Do family 331’s Lean statements express the advertised result?

These assessments compare all the family’s Lean statements with the whole result OpenAI describes in its catalogue. They do not assess this statement alone. Each group uses its own published rules.

MathVet and cert-machine agree on how much of the advertised result the Lean statements express.

  • MathVet

    Finds the whole advertised result stated or implied Their term: “full”.

    The challenge statements imply the headline claim after unpacking definitions or one routine step. Their record

    Their note: ForestSpace.main_theorem (unconditional) states for FullDual Vertex (continuous dual of the completed disjoint-segment-norm space over a forest): infinite-dimensional, separable, canonically reflexive, no equivalent norm is AUC, averaged midpoint modulus ≥ √(1+t²/12) - 1 for all t > 0 (this implies positivity of the usual max-version midpoint modulus, since max ≥ average: the one routine step), and every embedding of the depth-k countably branching diamond has distortion ≥ √(1+k/12). The other six challenges are companion results of the seven papers (lens tail bound, all-pairs diamond bound 1+k/4 ≤ D², exact Daugavet-subspace moduli, tree-potential, independent-product and recursive-potential spaces), not needed for the headline.

    Definitions the group asks readers to compare with the paper: aucModulus, averageModulus and Cofinite are hand-built (inf_{N x=1} sup_{F cofinite closed} inf_{y∈F, N y=1} (N(x+ty)-1), resp. with the average of N(x±ty) minus 1), matching the standard asymptotic moduli; IsAUC N := ∀ t > 0, 0 < aucModulus N t and EquivalentNorm as a seminorm with α‖x‖ ≤ N x ≤ β‖x‖ ; Diamond.diamond k := base.iterate k (each edge replaced by ℕ-many 2-edge paths, graph distance via SimpleGraph.dist) and EmbeddingBound (∃ scale s > 0, s·d ≤ ‖f u - f v‖ ≤ C·s·d): standard distortion; docs note 22 companion inputs are assumed in some graph deductions, not in the headline theorem

  • Carlos Toledo’s cert-machine

    Finds the whole advertised result stated or implied Their term: “MATCHES”.

    The challenge statements state the headline as OpenAI wrote it. Their record

Read the complete assessments on the family page.

OpenAI’s own note on what this Lean statement covers

“The paper forms a real $L_1$ space from the closed span of products of independent multipliers along tree paths. For a positive, nonconstant multiplier of mean one and finite second moment, the formalization proves that this space is infinite-dimensional and has no equivalent asymptotically uniformly convex norm.”

An AI agent drafted an interpretation of that note, classifying this statement as “states a paper’s theorem as the paper does”. No human review of the draft is recorded.

Supporting material

The complete Lean statement file

120 lines, copied from release version fd4aeeb. A name defined in this file links to its line; pointing at the name shows its definition.

Declared in this fileOpenAI’s library at fd4aeebOther linked names open the current documentation for Mathlib, Lean’s mathematical library.

Links in the file: 46 to its own declarations, 54 to Mathlib’s documentation. 21 declared names also link to the same declaration in OpenAI’s library. OpenAI uses Mathlib version d13f23b. The linked documentation describes the current version, so names that have since changed may be missing there.

import Mathlibnamespace OAInoncomputable sectionopen MeasureTheory ProbabilityTheory Set Filteropen scoped ENNReal NNReal Topologyuniverse uXnamespace IndependentProducts/-- The tree of finite sequences of positive integers. -/abbrev Vertex := List ℕ+/-- Coordinates are attached only to nonroot vertices. -/abbrev Edge := {v : Vertex // v ≠ []}abbrev Sample := Edge → ℝ/-- The product law of independent copies of the multiplier. -/def productMeasure (μ : Measure ℝ) : Measure Sample :=  Measure.infinitePi (fun _ : Edge => μ)/-- The nonroot prefix ending at position `j`. -/def pathPrefix (v : Vertex) (j : Fin v.length) : Edge :=  ⟨v.take (j.val + 1), by    intro h    have hl := congrArg List.length h    simp only [List.length_take, List.length_nil] at hl    have := j.isLt    omega⟩/-- Root product is the empty product 1; every other product uses the path prefixes. -/def pathProduct (v : Vertex) (ω : Sample) : ℝ :=  ∏ j : Fin v.length, ω (pathPrefix v j)/-- The closed real L1 span of the path products. -/def productSpan (μ : Measure ℝ) : Submodule ℝ (Lp ℝ 1 (productMeasure μ)) :=  (Submodule.span ℝ {f : Lp ℝ 1 (productMeasure μ) | ∃ v : Vertex,    (f : Sample → ℝ) =ᵐ[productMeasure μ] pathProduct v}).topologicalClosure/-- Positivity, nonconstancy, mean one, and finite second moment of the multiplier law. -/structure MultiplierHypotheses (μ : Measure ℝ) : Prop where  probability : IsProbabilityMeasure μ  positive : ∀ᵐ w ∂μ, 0 < w  nonconstant : ∀ c : ℝ, ¬ (∀ᵐ w ∂μ, w = c)  mean_one : ∫ w, w ∂μ = 1  second_moment : MemLp (fun w : ℝ => w) 2 μvariable (X : Type uX) [NormedAddCommGroup X] [NormedSpace ℝ X]/-- Closed subspaces of finite codimension, with respect to the inherited topology. -/def CofiniteSubspaces : Set (Submodule ℝ X) :=  {F | IsClosed (F : Set X) ∧ FiniteDimensional ℝ (X ⧸ F)}/-- The averaged asymptotic midpoint modulus, with directions of norm at least 1. -/def averagedMidpointModulus (N : Seminorm ℝ X) (t : ℝ) : ℝ :=  sInf {r | ∃ x : X, N x = 1 ∧ r =    sSup {a | ∃ F ∈ CofiniteSubspaces X, a =      sInf {b | ∃ y : X, y ∈ F ∧ 1 ≤ N y ∧        b = (N (x + t • y) + N (x - t • y)) / 2 - 1}}}/-- The usual one-sided modulus, whose directions have norm exactly 1. -/def oneSidedModulus (N : Seminorm ℝ X) (t : ℝ) : ℝ :=  sInf {r | ∃ x : X, N x = 1 ∧ r =    sSup {a | ∃ F ∈ CofiniteSubspaces X, a =      sInf {b | ∃ y : X, y ∈ F ∧ N y = 1 ∧ b = N (x + t • y) - 1}}}/-- A seminorm with these bounds is a genuine norm equivalent to the original norm. -/def IsEquivalentNorm (N : Seminorm ℝ X) : Prop :=  ∃ a b : ℝ, 0 < a ∧ a ≤ b ∧ ∀ x, a * ‖x‖ ≤ N x ∧ N x ≤ b * ‖x‖def IsAUC (N : Seminorm ℝ X) : Prop := ∀ t : ℝ, 0 < t → 0 < oneSidedModulus X N t/-- Gaussian-tail probability, in real rather than extended-nonnegative-real notation. -/def gaussianTail (r : ℝ) : ℝ :=  (gaussianReal 0 1 {x : ℝ | r ≤ |x|}).toReal/-- The Gaussian excess constant at threshold twice the parameter. -/def excessConstant (K : ℝ) : ℝ :=  (1 / 16 : ℝ) * ∫ x : ℝ, max (|x| - 2 * K) 0 ∂gaussianReal 0 1/-- The density exp(-w) on the positive half-line. -/def exponentialLaw : Measure ℝ := expMeasure 1/-- The law of the square of a standard real Gaussian. -/def gaussianSquareLaw : Measure ℝ := (gaussianReal 0 1).map (fun x : ℝ => x ^ 2)theorem main_general (μ : Measure ℝ) (hμ : MultiplierHypotheses μ) :    ¬ FiniteDimensional ℝ (productSpan μ) ∧    ∀ N : Seminorm ℝ (productSpan μ),      IsEquivalentNorm (productSpan μ) N → ¬ IsAUC (productSpan μ) N := by  sorrytheorem main_exponential (t : ℝ) (ht : 0 < t) :    (7 * t / 30) * excessConstant (10 / t) ≤      averagedMidpointModulus (productSpan exponentialLaw)        (normSeminorm ℝ (productSpan exponentialLaw)) t ∧    0 < (7 * t / 30) * excessConstant (10 / t) := by  sorrytheorem main_gaussian_24 (t : ℝ) (ht : 0 < t) :    gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 ≤      averagedMidpointModulus (productSpan gaussianSquareLaw)        (normSeminorm ℝ (productSpan gaussianSquareLaw)) t ∧    0 < gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 := by  sorrytheorem main_gaussian_12 (t : ℝ) (ht : 0 < t) :    gaussianTail (8 * Real.sqrt 6 / t) / 4 ≤      averagedMidpointModulus (productSpan gaussianSquareLaw)        (normSeminorm ℝ (productSpan gaussianSquareLaw)) t ∧    0 < gaussianTail (8 * Real.sqrt 6 / t) / 4 := by  sorryend IndependentProductsendend OAI
Every extracted statement of the accompanying papers

The statements the atlas extracted automatically from each accompanying paper’s TeX source, with links to the full sources.

Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space

Theorem

The space X=J^* defined by (eq:segment-norm) is infinite dimensional, separable, and reflexive, and has no equivalent AUC norm. Its given norm satisfies

\widehat\delta_X(t)\ge\sqrt{1+t^2/12}-1\qquad(t>0).

For every integer k\ge0, every embedding of D_k into X has distortion at least \sqrt{1+k/12}.

Section 1, Introduction Read it in the paper TeX source, line 99
Show the TeX
The space $X=J^*$ defined by \eqref{eq:segment-norm} is infinite
dimensional, separable, and reflexive, and has no equivalent AUC norm.
Its given norm satisfies
\[
 \widehat\delta_X(t)\ge\sqrt{1+t^2/12}-1\qquad(t>0).
\]
For every integer $k\ge0$, every embedding of $D_k$ into $X$ has
distortion at least $\sqrt{1+k/12}$.

Midpoint lenses in segment spaces

Theorem

Let Y be either the finite-height forest dual X=J^* or the infinite-height coordinate predual B_\infty. For every finite ancestral H, every x,y\in Y with x=P_Hx, and every R\ge0 with \|x+y\|,\|x-y\|\le R,

\|Q_Hy\|\le2\sqrt{R^2-\|x\|^2}.
Section 1, Introduction Read it in the paper TeX source, line 54
Show the TeX
Let $Y$ be either the finite-height forest dual $X=J^*$ or the
infinite-height coordinate predual $B_\infty$. For every finite
ancestral $H$, every $x,y\in Y$ with $x=P_Hx$, and every $R\ge0$
with $\|x+y\|,\|x-y\|\le R$,
\begin{equation}
 \|Q_Hy\|\le2\sqrt{R^2-\|x\|^2}.
\end{equation}

Distortion of countably branching diamonds from midpoint and tree energies

Theorem

For every integer k\ge0, every distortion-D embedding of \mathcal D_k into either X or B_\infty satisfies

D^2\ge1+k/4.
Section 1, Introduction Read it in the paper TeX source, line 53
Show the TeX
For every integer $k\ge0$, every distortion-$D$ embedding of
$\mathcal D_k$ into either $X$ or $B_\infty$ satisfies
\[
 D^2\ge1+k/4.
\]

Exact asymptotic moduli in a Daugavet subspace of L1

Theorem

Let E be an infinite-dimensional closed real subspace of L^1(\Omega,\mathbb P), where \mathbb P is a probability measure. Suppose its closed unit ball is totally bounded for convergence in measure and its inherited norm has the Daugavet property. Then every x\in S_E and t>0 satisfy

H_E(x,t)=\max\{t/2,t-1\},\qquad D_E(x,t)=\max\{0,t-2\}.

Consequently these are also the formulas for \widehat\delta_E(t) and \overline\delta_E(t). The space E admits no equivalent AUC norm.

Section 1, Introduction Read it in the paper TeX source, line 63
Show the TeX
Let $E$ be an infinite-dimensional closed real subspace of
$L^1(\Omega,\mathbb P)$, where $\mathbb P$ is a probability measure.
Suppose its closed unit ball is totally bounded for convergence in
measure and its inherited norm has the Daugavet property.  Then every
$x\in S_E$ and $t>0$ satisfy
\[
 H_E(x,t)=\max\{t/2,t-1\},\qquad
 D_E(x,t)=\max\{0,t-2\}.
\]
Consequently these are also the formulas for
$\widehat\delta_E(t)$ and $\overline\delta_E(t)$.  The space $E$ admits
no equivalent AUC norm.

Midpoint convexity from bounded tree potentials and path costs

Theorem

Each of the four completed spaces X_K is a separable infinite-dimensional real Banach space. For K\in\{K_{\rm s},K_{\rm p},K_{\rm a},K_{\rm g}\} and 0<t<1,

\widehat\delta_{X_K}(t)\ge\sqrt{1+t^2/4}-1.

None of these spaces admits an equivalent AUC norm.

Section 2, Four potential norms, one pair of inward tests Read it in the paper TeX source, line 63
Show the TeX
Each of the four completed spaces $X_K$ is a separable infinite-dimensional
real Banach space. For $K\in\{K_{\rm s},K_{\rm p},K_{\rm a},K_{\rm g}\}$
and $0<t<1$,
\begin{equation}
 \widehat\delta_{X_K}(t)\ge\sqrt{1+t^2/4}-1.
\end{equation}
None of these spaces admits an equivalent AUC norm.
Theorem

The real Banach space X_{\mathrm e} defined by (fr:tests) and (fr:norm) satisfies

\widehat\delta_{\|\cdot\|_{\mathrm e}}(t) \ge\frac{\sqrt{1+t^2/576}-1}{4}\qquad(0<t<1).

It admits no equivalent AUC norm. More precisely, if \alpha\|\cdot\|_{\mathrm e}\le N\le\beta\|\cdot\|_{\mathrm e}, where 0<\alpha\le\beta<\infty, then \overline\delta_N(\alpha/(4\beta))=0.

Section 4, A free root and a planar perturbation Read it in the paper TeX source, line 67
Show the TeX
The real Banach space $X_{\mathrm e}$ defined by \eqref{fr:tests} and
\eqref{fr:norm} satisfies
\begin{equation}
 \widehat\delta_{\|\cdot\|_{\mathrm e}}(t)
 \ge\frac{\sqrt{1+t^2/576}-1}{4}\qquad(0<t<1).
 
\end{equation}
It admits no equivalent AUC norm.  More precisely, if
$\alpha\|\cdot\|_{\mathrm e}\le N\le\beta\|\cdot\|_{\mathrm e}$,
where $0<\alpha\le\beta<\infty$, then
$\overline\delta_N(\alpha/(4\beta))=0$.
Theorem (Clipping estimate on a head and its complement)

If x,y\in X, P_Ax=x and P_Ay=0 for a finite head A, then, for every c>0,

\max\{\|x+y\|,\|x-y\|\} \ge\frac{\|x\|+c\|y\|/2}{\sqrt{1+4c^2}}.

In particular the left side is at least \sqrt{\|x\|^2+\|y\|^2/16}.

Section 5, Linear costs of root paths Read it in the paper TeX source, line 287
Show the TeX
If $x,y\in X$, $P_Ax=x$ and $P_Ay=0$ for a finite head $A$, then,
for every $c>0$,
\begin{equation}
 \max\{\|x+y\|,\|x-y\|\}
 \ge\frac{\|x\|+c\|y\|/2}{\sqrt{1+4c^2}}.
 
\end{equation}
In particular the left side is at least
$\sqrt{\|x\|^2+\|y\|^2/16}$.

Independent products in real L1: asymptotic midpoint convexity without AUC renormings

Theorem

For every multiplier W as above, the Banach space X_W is infinite dimensional and has no equivalent AUC norm. For the exponential and Gaussian-square laws, the inherited norms satisfy

\begin{aligned}\whdelta_{X_{\mathrm{exp}}}(t) &\ge \frac{7t}{30}c(10/t)>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge4\sqrt2(2+\sqrt2)/t\bigr)}{8}>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge8\sqrt6/t\bigr)}{4}>0 &&(t>0).\end{aligned}
Section 1, Introduction Read it in the paper TeX source, line 57
Show the TeX
For every multiplier $W$ as above, the Banach space $X_W$ is infinite
dimensional and has no equivalent AUC norm.
For the exponential and Gaussian-square laws, the inherited norms satisfy
\begin{align}
 \whdelta_{X_{\mathrm{exp}}}(t)
 &\ge \frac{7t}{30}c(10/t)>0 &&(t>0),\\
 \whdelta_{X_{\mathrm{sq}}}(t)
 &\ge\frac{\PP\bigl(|G|\ge4\sqrt2(2+\sqrt2)/t\bigr)}{8}>0
 &&(t>0),\\
 \whdelta_{X_{\mathrm{sq}}}(t)
 &\ge\frac{\PP\bigl(|G|\ge8\sqrt6/t\bigr)}{4}>0
 &&(t>0).
\end{align}

Midpoint convexity from two recursive potentials

Theorem

For N=N_\Sigma on X_\Sigma, or N=N_J on X_J, one has

\widehat\delta_N(t)\ge t^3/128\qquad(0<t<1).

For X_0, a finite initial set H, a vector x supported in H, and z\in F_H\setminus\{0\}, one has

\frac{N_0(x+z)+N_0(x-z)}2\ge N_0(x) +\frac{N_0(z)^3}{8(2N_0(x)+N_0(z))^2}.

Each of X_\Sigma,X_0,X_J admits no equivalent AUC norm.

Section 2, The fields, the roots, and the completed spaces Read it in the paper TeX source, line 65
Show the TeX
For $N=N_\Sigma$ on $X_\Sigma$, or $N=N_J$ on $X_J$, one has
\begin{equation}
 \widehat\delta_N(t)\ge t^3/128\qquad(0<t<1).
\end{equation}
For $X_0$, a finite initial set $H$, a vector $x$ supported in $H$, and
$z\in F_H\setminus\{0\}$, one has
\begin{equation}
 \frac{N_0(x+z)+N_0(x-z)}2\ge N_0(x)
 +\frac{N_0(z)^3}{8(2N_0(x)+N_0(z))^2}.
\end{equation}
Each of $X_\Sigma,X_0,X_J$ admits no equivalent AUC norm.
Theorem (Separated midpoint families)

For every \varepsilon>0 there is \eta(\varepsilon)\in(0,1) such that, for x,z_1,z_2,\ldots\in X_J,

\left. \begin{gathered} N_J(x+z_j),N_J(x-z_j)\le1\quad(j\ge1),\\ N_J(z_i-z_j)\ge\varepsilon\quad(i\ne j) \end{gathered} \right\} \quad\Longrightarrow\quad N_J(x)\le1-\eta(\varepsilon).

For 0<\varepsilon\le2, one can take \eta(\varepsilon)=\min\{1/2,\gamma(2,\varepsilon/16)\}.

Section 6, Logarithmic propagation on the joined tree Read it in the paper TeX source, line 480
Show the TeX
For every $\varepsilon>0$ there is $\eta(\varepsilon)\in(0,1)$ such
that, for $x,z_1,z_2,\ldots\in X_J$,
\[
 \left.
 \begin{gathered}
 N_J(x+z_j),N_J(x-z_j)\le1\quad(j\ge1),\\
 N_J(z_i-z_j)\ge\varepsilon\quad(i\ne j)
 \end{gathered}
 \right\}
 \quad\Longrightarrow\quad N_J(x)\le1-\eta(\varepsilon).
\]
For $0<\varepsilon\le2$, one can take
$\eta(\varepsilon)=\min\{1/2,\gamma(2,\varepsilon/16)\}$.
Theorem

Let x\in X_{\mathrm v} have finite total support. In each component where x^{(h)}\ne0, choose a finite ancestral set A_h containing that support. Suppose y\in X_{\mathrm v} vanishes on every such A_h. With S=\|x+y\|_{\mathrm v}^2+\|x-y\|_{\mathrm v}^2, one has

S-2\|x\|_{\mathrm v}^2 \ge c_6 S^{-2}\|y\|_{\mathrm v}^6, \qquad c_6=2\exp(-(\mathrm e-1))4^{-6}.

The right side is zero when S=0.

Section 7, Changing the child exponent with the height Read it in the paper TeX source, line 294
Show the TeX
Let $x\in X_{\mathrm v}$ have finite total support.  In each component
where $x^{(h)}\ne0$, choose a finite ancestral set $A_h$ containing
that support.  Suppose $y\in X_{\mathrm v}$ vanishes on every such
$A_h$.  With
$S=\|x+y\|_{\mathrm v}^2+\|x-y\|_{\mathrm v}^2$, one has
\begin{equation}
 S-2\|x\|_{\mathrm v}^2
 \ge c_6 S^{-2}\|y\|_{\mathrm v}^6,
 \qquad c_6=2\exp(-(\mathrm e-1))4^{-6}.
\end{equation}
The right side is zero when $S=0$.
Configuration and file facts
OpenAI’s Lean catalogue
OpenAI lists this as a main result in its Lean catalogue.
Allowed axioms
propext, Quot.sound, Classical.choiceThe axioms this configuration allows a proof to use. A replay shows which of them a proof uses.
Imports
Mathlib
Imports from outside Mathlib
None
Local vocabulary
17 local vocabulary declarations
Lines of Lean, without comments or blank lines
66
Second proof-verification program (nanoda)
Off in OpenAI's configuration.

Statement comparison

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Plectis Atlas

Independent-product spaces and positive midpoint moduli

Family 331: Reflexive midpoint convexity and diamond distortion

Candidate pairing

Theorem

For every multiplier W as above, the Banach space X_W is infinite dimensional and has no equivalent AUC norm. For the exponential and Gaussian-square laws, the inherited norms satisfy

\begin{aligned}\whdelta_{X_{\mathrm{exp}}}(t) &\ge \frac{7t}{30}c(10/t)>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge4\sqrt2(2+\sqrt2)/t\bigr)}{8}>0 &&(t>0),\\ \whdelta_{X_{\mathrm{sq}}}(t) &\ge\frac{\PP\bigl(|G|\ge8\sqrt6/t\bigr)}{4}>0 &&(t>0).\end{aligned}

Independent products in real L1: asymptotic midpoint convexity without AUC renormings

TeX label thm:main, build/sections/introduction.tex, line 57

The Lean statement says

theorem main_general (μ : Measure ℝ) (hμ : MultiplierHypotheses μ) :    ¬ FiniteDimensional ℝ (productSpan μ) ∧    ∀ N : Seminorm ℝ (productSpan μ),      IsEquivalentNorm (productSpan μ) N → ¬ IsAUC (productSpan μ) N := by  sorrytheorem main_exponential (t : ℝ) (ht : 0 < t) :    (7 * t / 30) * excessConstant (10 / t) ≤      averagedMidpointModulus (productSpan exponentialLaw)        (normSeminorm ℝ (productSpan exponentialLaw)) t ∧    0 < (7 * t / 30) * excessConstant (10 / t) := by  sorrytheorem main_gaussian_24 (t : ℝ) (ht : 0 < t) :    gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 ≤      averagedMidpointModulus (productSpan gaussianSquareLaw)        (normSeminorm ℝ (productSpan gaussianSquareLaw)) t ∧    0 < gaussianTail (4 * Real.sqrt 2 * (2 + Real.sqrt 2) / t) / 8 := by  sorrytheorem main_gaussian_12 (t : ℝ) (ht : 0 < t) :

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17 local vocabulary declarationsAllowed axioms: propext, Quot.sound, Classical.choiceopenai/math at fd4aeebwcook04.github.io/plectis-atlas/statements/IndependentProducts.html

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