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Lean statement

Uniform short Hilbert-transform bounds for Lipschitz directions

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.LipschitzHilbert.main OAI.LipschitzHilbert.uniform_truncation

Lean statement file: LipschitzHilbert.leanShow on the mapOpenAI’s Lean catalogue does not list this as a main result; the release’s Lean notes for family 083 link it.

The Lean statement

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

theorem main :    ∃ a : ℝ, a∈Ioo 0 (1/2) ∧ ∃ C : ℝ≥0, 0<C ∧ ShortHilbertConclusion a C := by  sorrytheorem uniform_truncation :    ∃ C : ℝ, 0<C ∧ ∀ K : ℝ≥0, 0<K → ∀ epsilon R : ℝ,      0<epsilon → epsilon≤R → R≤1/(1000000*(K:ℝ)) →      ∀ v : Plane → Plane, LipschitzWith K v → (∀ x, ‖v x‖=1) →      ∀ f : SchwartzMap Plane ℂ,        eLpNorm (shortTransform v epsilon R f) 2 volume≤ENNReal.ofReal (C*‖f.toLp 2 volume‖) := 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.LipschitzHilbert.Plane line 15

    abbrev Plane := EuclideanSpace ℝ (Fin 2)
  • abbreviation OAI.LipschitzHilbert.DirectionPlane line 16

    abbrev DirectionPlane := EuclideanSpace ℝ (Fin 2)
  • definition OAI.LipschitzHilbert.sample line 18

    def sample (v : DirectionPlane → DirectionPlane) (t : ℝ) (x : DirectionPlane) :    DirectionPlane := x - t • v x
  • definition OAI.LipschitzHilbert.shortTransform line 22

    def shortTransform (v : DirectionPlane → DirectionPlane) (ε a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  ∫ t in ε..a, t⁻¹ • (f (sample v t x) - f (sample v (-t) x))
  • definition OAI.LipschitzHilbert.directionalKernel line 26

    def directionalKernel (v : DirectionPlane → DirectionPlane)    (f : DirectionPlane → ℂ) (x : DirectionPlane) (t : ℝ) : ℂ :=  t⁻¹ • f (sample v t x)
  • definition OAI.LipschitzHilbert.twoSidedTruncation line 31

    def twoSidedTruncation (v : DirectionPlane → DirectionPlane) (ε a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  (∫ t in -a..-ε, directionalKernel v f x t) +    ∫ t in ε..a, directionalKernel v f x t
  • definition OAI.LipschitzHilbert.smoothPrincipalValue line 37

    def smoothPrincipalValue (v : DirectionPlane → DirectionPlane) (a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  let g := fun t : ℝ => f (sample v t x)-f (sample v (-t) x)  ∫ t in (0:ℝ)..a, Function.update (fun t => t⁻¹ • g t) 0 (deriv g 0) t
  • definition OAI.LipschitzHilbert.ShortHilbertConclusion line 44

    def ShortHilbertConclusion (a : ℝ) (C : ℝ≥0) : Prop :=
    Show the remaining 15 lines
      ∀ v : Plane → Plane, LipschitzWith 1 v → (∀ x, ‖v x‖=1) →    (∀ epsilon : ℝ, 0<epsilon → epsilon<a → ∀ f : SchwartzMap Plane ℂ,      eLpNorm (twoSidedTruncation v epsilon a f) 2 volume≤C*eLpNorm f 2 volume) ∧    (∀ f : SchwartzMap Plane ℂ,      eLpNorm (smoothPrincipalValue v a f) 2 volume≤C*eLpNorm f 2 volume) ∧    (∀ f : SchwartzMap Plane ℂ, ∀ level : ℝ≥0∞, level≠0 → level≠∞ →      volume {x | level<‖smoothPrincipalValue v a f x‖ₑ}≤        level⁻¹^2*((C:ℝ≥0∞)*eLpNorm f 2 volume)^2) ∧    (∀ epsilon : ℝ, 0<epsilon → epsilon<a →      ∃ T : Lp ℂ 2 (volume : Measure Plane) →L[ℂ] Lp ℂ 2 volume,        ‖T‖≤C ∧ ∀ f : SchwartzMap Plane ℂ,          (T (f.toLp 2 volume) : Plane → ℂ)=ᵐ[volume] twoSidedTruncation v epsilon a f) ∧    (∃ T : Lp ℂ 2 (volume : Measure Plane) →L[ℂ] Lp ℂ 2 volume,      ‖T‖≤C ∧ ∀ f : SchwartzMap Plane ℂ,        (T (f.toLp 2 volume) : Plane → ℂ)=ᵐ[volume] smoothPrincipalValue v a f)

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: principal, value, symmetric, 1/2, field, lipschitz, epsilon and norm. Found in the paper’s title, which its main theorem states: directions, transform, uniform, hilbert and estimate. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: constants.

Theorem 1.1 of A uniform Hilbert transform estimate for Lipschitz directions

Theorem

There are absolute constants a_*\in(0,1/2) and C_*<\infty such that every Lipschitz field v:\R^2\to S^1 with \Lip(v)\le1 satisfies

\sup_{0<\epsilon<a_*} \norm{H_{v,a_*}^{\epsilon}f}_{L^2(\R^2)} \le C_*\norm{f}_{L^2(\R^2)} \qquad(f\in\mathcal S(\R^2)).

Its symmetric principal value therefore satisfies

\norm{H_{v,a_*}f}_2\le C_*\norm f_2, \qquad \big|\{x:|H_{v,a_*}f(x)|>\lambda\}\big| \le C_*^2\lambda^{-2}\norm f_2^2 \quad(\lambda>0).
Section 1, Short transforms and band reduction Read it in the paper TeX source, line 17
Show the TeX
There are absolute constants \(a_*\in(0,1/2)\) and \(C_*<\infty\)
such that every Lipschitz field \(v:\R^2\to S^1\) with
\(\Lip(v)\le1\) satisfies
\[
 \sup_{0<\epsilon<a_*}
 \norm{H_{v,a_*}^{\epsilon}f}_{L^2(\R^2)}
 \le C_*\norm{f}_{L^2(\R^2)}
 \qquad(f\in\mathcal S(\R^2)).
\]
Its symmetric principal value therefore satisfies
\[
 \norm{H_{v,a_*}f}_2\le C_*\norm f_2,
 \qquad
 \big|\{x:|H_{v,a_*}f(x)|>\lambda\}\big|
 \le C_*^2\lambda^{-2}\norm f_2^2
 \quad(\lambda>0).
\]

Other possible paper matches, closest wording first

  • Proposition 1.9 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: uniform and estimate. Found in the paper’s title, which its main theorem states: directions, transform, lipschitz and hilbert.
  • Proposition 1.15 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: input, bounded, field, scales, form, epsilon and four more. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: constants and one.
  • Proposition 2.1 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: conclusions, 1/2, space, hilbert, estimates and norm. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: one.
  • Theorem 7.5 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: real, bounded, forms, interval, measurable and estimates. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: one.
  • Proposition 4.1 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: complex, space, epsilon, hilbert, estimates, measure and one more. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: one and constant.
  • Theorem 1.4 of A uniform Hilbert transform estimate for Lipschitz directions. Words of the Lean statement, its names split into words, that also appear in the theorem: field, cutoff, epsilon, estimate and measure. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: constants and one.

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

One independent group reports that Comparator accepted a run for this Lean statement file: Carlos Toledo’s cert-machine. 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

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, Classical.choice, Quot.sound

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 083’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: None; statement is exactly the short-scale uniform bound plus principal-value/weak-type consequences (not the full-scale Stein conjecture).

    Definitions the group asks readers to compare with the paper: smoothPrincipalValue extends the symmetric quotient at 0 by Function.update ... 0 (deriv g 0) (custom, correct for Schwartz f); file temporarily removes/re-adds the instance instCommCStarAlgebraComplex to fix scalar-multiplication elaboration (harmless hack)

  • 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 formalization proves uniform $L^2$ bounds for short Hilbert transforms along Lipschitz unit vector fields in the plane, including fields depending on both coordinates. For Lipschitz constant $K>0$, every hard truncation with $0<\varepsilon\le R\le1/(10^6K)$ has one universal $L^2$ bound on Schwartz functions, independent of the inner cutoff.”

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

80 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: 48 to its own declarations, 30 to Mathlib’s documentation. 10 declared names also link to the same declaration in OpenAI’s library. 2 identifiers stay plain because they resolve to no single declaration: variables Lean binds automatically, notation, or names the scan of the pinned sources did not find. 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 section/- Use the normed-field structure on ℂ for real scalar multiplication. -/attribute [-instance] instCommCStarAlgebraComplexopen MeasureTheory Setopen scoped ENNReal NNRealnamespace LipschitzHilbertabbrev Plane := EuclideanSpace ℝ (Fin 2)abbrev DirectionPlane := EuclideanSpace ℝ (Fin 2)def sample (v : DirectionPlane → DirectionPlane) (t : ℝ) (x : DirectionPlane) :    DirectionPlane := x - t • v x/-- The symmetrized form of the hard-truncated directional integral. -/def shortTransform (v : DirectionPlane → DirectionPlane) (ε a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  ∫ t in ε..a, t⁻¹ • (f (sample v t x) - f (sample v (-t) x))def directionalKernel (v : DirectionPlane → DirectionPlane)    (f : DirectionPlane → ℂ) (x : DirectionPlane) (t : ℝ) : ℂ :=  t⁻¹ • f (sample v t x)/-- The two hard-cutoff intervals in the directional Hilbert transform. -/def twoSidedTruncation (v : DirectionPlane → DirectionPlane) (ε a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  (∫ t in -a..-ε, directionalKernel v f x t) +    ∫ t in ε..a, directionalKernel v f x t/-- The symmetric quotient extended continuously at zero for smooth inputs. -/def smoothPrincipalValue (v : DirectionPlane → DirectionPlane) (a : ℝ)    (f : DirectionPlane → ℂ) (x : DirectionPlane) : ℂ :=  let g := fun t : ℝ => f (sample v t x)-f (sample v (-t) x)  ∫ t in (0:ℝ)..a, Function.update (fun t => t⁻¹ • g t) 0 (deriv g 0) tattribute [instance] instCommCStarAlgebraComplexdef ShortHilbertConclusion (a : ℝ) (C : ℝ≥0) : Prop :=  ∀ v : Plane → Plane, LipschitzWith 1 v → (∀ x, ‖v x‖=1) →    (∀ epsilon : ℝ, 0<epsilon → epsilon<a → ∀ f : SchwartzMap Plane ℂ,      eLpNorm (twoSidedTruncation v epsilon a f) 2 volume≤C*eLpNorm f 2 volume) ∧    (∀ f : SchwartzMap Plane ℂ,      eLpNorm (smoothPrincipalValue v a f) 2 volume≤C*eLpNorm f 2 volume) ∧    (∀ f : SchwartzMap Plane ℂ, ∀ level : ℝ≥0∞, level≠0 → level≠∞ →      volume {x | level<‖smoothPrincipalValue v a f x‖ₑ}≤        level⁻¹^2*((C:ℝ≥0∞)*eLpNorm f 2 volume)^2) ∧    (∀ epsilon : ℝ, 0<epsilon → epsilon<a →      ∃ T : Lp ℂ 2 (volume : Measure Plane) →L[ℂ] Lp ℂ 2 volume,        ‖T‖≤C ∧ ∀ f : SchwartzMap Plane ℂ,          (T (f.toLp 2 volume) : Plane → ℂ)=ᵐ[volume] twoSidedTruncation v epsilon a f) ∧    (∃ T : Lp ℂ 2 (volume : Measure Plane) →L[ℂ] Lp ℂ 2 volume,      ‖T‖≤C ∧ ∀ f : SchwartzMap Plane ℂ,        (T (f.toLp 2 volume) : Plane → ℂ)=ᵐ[volume] smoothPrincipalValue v a f)/-- Uniform hard truncation, principal-value and weak-(2,2) estimates,with bounded L² extensions for all Lipschitz unit vector fields. -/theorem main :    ∃ a : ℝ, a∈Ioo 0 (1/2) ∧ ∃ C : ℝ≥0, 0<C ∧ ShortHilbertConclusion a C := by  sorry/-- The same uniform estimate at the reciprocal-Lipschitz truncation scale. -/theorem uniform_truncation :    ∃ C : ℝ, 0<C ∧ ∀ K : ℝ≥0, 0<K → ∀ epsilon R : ℝ,      0<epsilon → epsilon≤R → R≤1/(1000000*(K:ℝ)) →      ∀ v : Plane → Plane, LipschitzWith K v → (∀ x, ‖v x‖=1) →      ∀ f : SchwartzMap Plane ℂ,        eLpNorm (shortTransform v epsilon R f) 2 volume≤ENNReal.ofReal (C*‖f.toLp 2 volume‖) := by  sorryend LipschitzHilbertendend 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.

A uniform Hilbert transform estimate for Lipschitz directions

Theorem

There are absolute constants a_*\in(0,1/2) and C_*<\infty such that every Lipschitz field v:\R^2\to S^1 with \Lip(v)\le1 satisfies

\sup_{0<\epsilon<a_*} \norm{H_{v,a_*}^{\epsilon}f}_{L^2(\R^2)} \le C_*\norm{f}_{L^2(\R^2)} \qquad(f\in\mathcal S(\R^2)).

Its symmetric principal value therefore satisfies

\norm{H_{v,a_*}f}_2\le C_*\norm f_2, \qquad \big|\{x:|H_{v,a_*}f(x)|>\lambda\}\big| \le C_*^2\lambda^{-2}\norm f_2^2 \quad(\lambda>0).
Section 1, Short transforms and band reduction Read it in the paper TeX source, line 17
Show the TeX
There are absolute constants \(a_*\in(0,1/2)\) and \(C_*<\infty\)
such that every Lipschitz field \(v:\R^2\to S^1\) with
\(\Lip(v)\le1\) satisfies
\[
 \sup_{0<\epsilon<a_*}
 \norm{H_{v,a_*}^{\epsilon}f}_{L^2(\R^2)}
 \le C_*\norm{f}_{L^2(\R^2)}
 \qquad(f\in\mathcal S(\R^2)).
\]
Its symmetric principal value therefore satisfies
\[
 \norm{H_{v,a_*}f}_2\le C_*\norm f_2,
 \qquad
 \big|\{x:|H_{v,a_*}f(x)|>\lambda\}\big|
 \le C_*^2\lambda^{-2}\norm f_2^2
 \quad(\lambda>0).
\]
Theorem (Lacey–Li)

Let v:\R^2\to S^1 be Lipschitz and 0<\delta<1. Restrict to rectangles satisfying

L(R)\le \frac1{100\Lip(v)},\qquad \abs{R\cap v^{-1}(\operatorname{EX}(R))}\ge\delta\abs R,

where the first restriction is void when \Lip(v)=0. Then

\mathcal M_{v,\delta}h(z) =\sup_R\frac{\one_R(z)}{\abs R}\int_R\abs h \quad\text{satisfies}\quad \norm{\mathcal M_{v,\delta}h}_{L^{2,\infty}} \le C\delta^{-1/2}\norm h_2.

Here the supremum is tested at every point of R, and the constant is independent of v and \delta.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 26
Show the TeX
Let \(v:\R^2\to S^1\) be Lipschitz and \(0<\delta<1\). Restrict to
rectangles satisfying
\[
 L(R)\le \frac1{100\Lip(v)},\qquad
 \abs{R\cap v^{-1}(\operatorname{EX}(R))}\ge\delta\abs R,
\]
where the first restriction is void when \(\Lip(v)=0\). Then
\[
 \mathcal M_{v,\delta}h(z)
   =\sup_R\frac{\one_R(z)}{\abs R}\int_R\abs h
 \quad\text{satisfies}\quad
 \norm{\mathcal M_{v,\delta}h}_{L^{2,\infty}}
       \le C\delta^{-1/2}\norm h_2.
\]
Here the supremum is tested at every point of \(R\), and the constant
is independent of \(v\) and \(\delta\).
Theorem (Restricted estimate on a vertical band)

For the fixed absolute constants in (red:local-operator), there is an absolute C such that, for every field u with \abs u\le1 and \Lip(u)\le C_{\rm Lip}, every 0<\epsilon<a, every dyadic w, and every vertical band cutoff P_w,

\abs{\langle H_{u,a}^{\epsilon}P_w f,g\rangle} \le C\bigl(1+\abs{\log(|F|/|E|)}\bigr)^{-3} \sqrt{|F||E|}

whenever F,E\subset I\times\R have positive finite measure, \abs f\le\one_F, \abs g\le\one_E, and \abs I\le b_0.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 182
Show the TeX
For the fixed absolute constants in \eqref{red:local-operator}, there
is an absolute \(C\) such that, for every field \(u\) with
\(\abs u\le1\) and \(\Lip(u)\le C_{\rm Lip}\), every
\(0<\epsilon<a\), every dyadic \(w\), and every vertical band
cutoff \(P_w\),
\begin{equation}
 \abs{\langle H_{u,a}^{\epsilon}P_w f,g\rangle}
 \le C\bigl(1+\abs{\log(|F|/|E|)}\bigr)^{-3}
       \sqrt{|F||E|}
\end{equation}
whenever \(F,E\subset I\times\R\) have positive finite measure,
\(\abs f\le\one_F\), \(\abs g\le\one_E\), and
\(\abs I\le b_0\).
Corollary

For \abs t\le b_0 and T as in red:commutator,

\norm{TC_t h-C_tTh}_2 \le C A(\mathfrak m)\abs t\,\norm h_2.

The constant is uniform over \abs u\le1, \Lip(u)\le C_{\rm Lip}.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 469
Show the TeX
For \(\abs t\le b_0\) and \(T\) as in \Cref{red:commutator},
\begin{equation}
 \norm{TC_t h-C_tTh}_2
      \le C A(\mathfrak m)\abs t\,\norm h_2.
\end{equation}
The constant is uniform over \(\abs u\le1\),
\(\Lip(u)\le C_{\rm Lip}\).
Proposition

The uniform restricted estimate in thm:band-restricted implies thm:main.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 565
Show the TeX
The uniform restricted estimate in \Cref{thm:band-restricted} implies
\Cref{thm:main}.
Proposition (Packet representation)

The diagonal form

\sum_j\langle\mathcal A_{u,\mathcal L} \widetilde Q_j f_j,g_j\rangle

is a sum, with absolutely summable scalar coefficients, of averages of forms (eq:src-3). Each form satisfies red:packets, uniformly in w,m,\mathcal L, and has one common angular grid and common spatial lattices across components. The same assertion holds for m=1 with the original positive-frequency band cutoff in place of \widetilde Q_1. The sums can be obtained as limits of finite packet collections.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 1,005
Show the TeX
The diagonal form
\[
 \sum_j\langle\mathcal A_{u,\mathcal L}
                         \widetilde Q_j f_j,g_j\rangle
\]
is a sum, with absolutely summable scalar coefficients, of averages
of forms \eqref{eq:src-3}. Each form satisfies
\Cref{red:packets}, uniformly in \(w,m,\mathcal L\), and has one
common angular grid and common spatial lattices across components.
The same assertion holds for \(m=1\) with the original
positive-frequency band cutoff in place of \(\widetilde Q_1\).
The sums can be obtained as limits of finite packet collections.
Proposition

Suppose that the following bounds hold, uniformly for every admissible finite form \Lambda_{\mathcal S} in red:packets, with the fixed packet constants there. The field u:\R^2\to\R is bounded and measurable, the sets F,E\subset\R^2 are measurable with positive finite measure, and the input and test satisfy \norm{f(z)}_{\mathcal H}\le\one_F(z), \norm{g(z)}_{\mathcal H}\le\one_E(z).

  1. (i)

    For m\ge1, there is a fixed exponent C_1 such that

    |\Lambda_{\mathcal S}(f,g)| \le C(1+\log(2m))^{C_1}\sqrt{|F||E|}.
  2. (ii)

    For L\ge L_0, with \tau,m,W as in (red:small-time-parameters), assume in addition that \abs u\le1, \Lip(u)\le C_{\rm Lip}, and F,E\subset I\times\R for an interval I with \abs I\le b_0. Whenever L/2\le\log\sqrt{|E|/|F|}\le2L,

    |\Lambda_{\mathcal S}(f,g)| \le C L^{-20}\sqrt{|F||E|}.
  3. (iii)

    For m=1, one scalar component, and |E|\le|F|, there is a fixed c>0 such that

    |\Lambda_{\mathcal S}(f,g)| \le C\sqrt{|F||E|}(|E|/|F|)^c.

Then thm:band-restricted holds. The constants remain uniform in w, \epsilon, and the number of retained scales.

Section 1, Short transforms and band reduction Read it in the paper TeX source, line 1,186
Show the TeX
Suppose that the following bounds hold, uniformly for every admissible
finite form \(\Lambda_{\mathcal S}\) in
\Cref{red:packets}, with the fixed packet constants there.
The field \(u:\R^2\to\R\) is bounded and measurable, the sets
\(F,E\subset\R^2\) are measurable with positive finite measure,
and the input and test satisfy
\(\norm{f(z)}_{\mathcal H}\le\one_F(z)\),
\(\norm{g(z)}_{\mathcal H}\le\one_E(z)\).
\begin{enumerate}[label=\textup{(\roman*)}]
\item For \(m\ge1\), there is a fixed exponent \(C_1\) such that
\begin{equation}
 |\Lambda_{\mathcal S}(f,g)|
     \le C(1+\log(2m))^{C_1}\sqrt{|F||E|}.
\end{equation}
\item For \(L\ge L_0\), with \(\tau,m,W\) as in
\eqref{red:small-time-parameters}, assume in addition that
\(\abs u\le1\), \(\Lip(u)\le C_{\rm Lip}\), and
\(F,E\subset I\times\R\) for an interval \(I\) with
\(\abs I\le b_0\). Whenever
\(L/2\le\log\sqrt{|E|/|F|}\le2L\),
\begin{equation}
 |\Lambda_{\mathcal S}(f,g)|
       \le C L^{-20}\sqrt{|F||E|}.
\end{equation}
\item For \(m=1\), one scalar component, and \(|E|\le|F|\),
there is a fixed \(c>0\) such that
\begin{equation}
 |\Lambda_{\mathcal S}(f,g)|
       \le C\sqrt{|F||E|}(|E|/|F|)^c.
\end{equation}
\end{enumerate}
Then \Cref{thm:band-restricted} holds. The constants remain uniform
in \(w\), \(\epsilon\), and the number of retained scales.
Configuration and file facts
OpenAI’s Lean catalogue
OpenAI’s Lean catalogue does not list this as a main result; the release’s Lean notes for family 083 link it.
Solution module
OAI.Analysis.LipschitzHilbert.Main
Allowed axioms
propext, Classical.choice, Quot.soundThe axioms this configuration allows a proof to use. A replay shows which of them a proof uses.
Imports
Mathlib
Imports from outside Mathlib
None
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8 local vocabulary declarations
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Uniform short Hilbert-transform bounds for Lipschitz directions

Family 083: Hilbert transforms along Lipschitz directions

Candidate pairing

Theorem

There are absolute constants a_*\in(0,1/2) and C_*<\infty such that every Lipschitz field v:\R^2\to S^1 with \Lip(v)\le1 satisfies

\sup_{0<\epsilon<a_*} \norm{H_{v,a_*}^{\epsilon}f}_{L^2(\R^2)} \le C_*\norm{f}_{L^2(\R^2)} \qquad(f\in\mathcal S(\R^2)).

Its symmetric principal value therefore satisfies

\norm{H_{v,a_*}f}_2\le C_*\norm f_2, \qquad \big|\{x:|H_{v,a_*}f(x)|>\lambda\}\big| \le C_*^2\lambda^{-2}\norm f_2^2 \quad(\lambda>0).

A uniform Hilbert transform estimate for Lipschitz directions

TeX label thm:main, build/sections/01-introduction.tex, line 17

The Lean statement says

theorem main :    ∃ a : ℝ, a∈Ioo 0 (1/2) ∧ ∃ C : ℝ≥0, 0<C ∧ ShortHilbertConclusion a C := by  sorrytheorem uniform_truncation :    ∃ C : ℝ, 0<C ∧ ∀ K : ℝ≥0, 0<K → ∀ epsilon R : ℝ,      0<epsilon → epsilon≤R → R≤1/(1000000*(K:ℝ)) →      ∀ v : Plane → Plane, LipschitzWith K v → (∀ x, ‖v x‖=1) →      ∀ f : SchwartzMap Plane ℂ,        eLpNorm (shortTransform v epsilon R f) 2 volume≤ENNReal.ofReal (C*‖f.toLp 2 volume‖) := by  sorry

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8 local vocabulary declarationsAllowed axioms: propext, Classical.choice, Quot.soundopenai/math at fd4aeebwcook04.github.io/plectis-atlas/statements/LipschitzHilbert.html

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