Strict four-row permanent inequality near the uniform law
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Theorem selected for verification:OAI.FourRow.robust_permanent
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Definitions needed to read it
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defpermanentExpectation (ν : Law) (f : Functions) : ℝ := ∑ π, ν π * ∏ i, f i (π i)
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: 4/3, robust, functions, pi, 1/4 and probability. Found in the paper’s title, which its main theorem states: permanent. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: inequality, four, nonnegative, varepsilon, row and constants.
Statements in A strict four-row permanent inequality and permutation moments come first because the release’s name for this Lean statement shares words with that paper’s title.
Theorem 1.1 of A strict four-row permanent inequality and permutation moments
Theorem(A robust four-row inequality)
There are absolute constants p_0\in(4/3,2) and \varepsilon>0 with the following property. Let \nu be a probability measure on S_4 such that
There are absolute constants $p_0\in(4/3,2)$ and $\varepsilon>0$ with the following property. Let $\nu$ be a probability measure on $S_4$ such that
\[
\|\nu-u\|_{\TV}<\varepsilon,
\qquad
\nu\{\pi:\pi(i)=j\}=\frac14\quad(1\le i,j\le4).
\]
Then every four nonnegative functions on $\{1,2,3,4\}$ satisfy
\begin{equation}
\E_\nu\prod_{i=1}^4f_i(\pi(i))\le\prod_{i=1}^4\|f_i\|_{p_0}.
\end{equation}
Other possible paper matches, closest wording first
Corollary 10.3 of Compatibility entropy and the spectrum of a Thorp sweep. Words of the Lean statement, its names split into words, that also appear in the theorem: functions, norms, marginal, probabilities and uniform. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: nonnegative, row and constant.
Theorem 1.1 of Compatibility entropy and the spectrum of a Thorp sweep. Words of the Lean statement, its names split into words, that also appear in the theorem: functions, expectations and uniform. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: nonnegative and constants.
Proposition 17.3 of Conditional information under deterministic coordinate sweeps. Words of the Lean statement, its names split into words, that also appear in the theorem: expectation, norms and real. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: matrix, row and bound.
Theorem 13.1 of Random coordinate frames and partial permutation laws. Words of the Lean statement, its names split into words, that also appear in the theorem: 1/4, probability, total, variation and uniform. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: mix and thorp.
Proposition 6.6 of Conditional information under deterministic coordinate sweeps. Words of the Lean statement, its names split into words, that also appear in the theorem: marginals, total, variation and uniform. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: bounds.
Theorem 1.1 of Conditional information under deterministic coordinate sweeps. Words of the Lean statement, its names split into words, that also appear in the theorem: total, variation and law. Shared only with the release’s name for the Lean statement or OpenAI’s note on it: consequence and distance.
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AcceptedTheir term: “comparator-sandboxed-pass”.
Re-run through Comparator in a sandboxed virtual machine: accepted.
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AcceptedTheir term: “accepted”.
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Do family 238’s Lean statements express the advertised result?
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Finds the whole advertised result stated or impliedTheir term: “full”.
The challenge statements imply the headline claim after unpacking definitions or one routine step. Their record
Their note: ThorpResults.remaining_main (ThorpRemaining) is a conjunction whose last conjunct OptimalOrderMain := IsTheta atTop (mixingTime d) (log (2^d)) states exactly the Θ(log N) mixing for N = 2^d cards, with mixingTime d := sInf {t | distance d t ≤ 1/4}, distance d t := tv (law d t) (uniform d) the total-variation distance of the full permutation law (and InformationMain records that lawFrom from any starting deck has the same distance, plus distance d (2048 d) → 0); the 12 other challenge files are supporting Fourier/representation estimates (regular_trace_smoothing, binary_sweep_contraction_and_mixing, robust_permanent, ...). All of them state estimates for the same hand-built Thorp shuffle.
Definitions the group asks readers to compare with the paper: Custom Thorp shuffle and 'mixing time': step (d+1) c := (pairSwitch d c on the head bit, then rotate the coordinate order) on State d := Equiv.Perm (Fin d → Bool) with Coins (d+1) = (Fin d → Bool) → Bool iid fair coins, fairMass, tv, mixingTime d := sInf {t | distance d t ≤ 1/4}; this is the standard pair-swap-and-interleave Thorp shuffle and TV-from-uniform with ε = 1/4, but it is a hand-written model and the Θ-statement hides explicit constants (1600d, 2048d, ...). OccupiedOverlap shows an axiom_files hit (HookModel.lean) that is a doc-comment false positive.
OpenAI’s own note on what this Lean statement covers
“The formalization proves the paper's strict four-row permanent inequality. There are constants $4/3<p<2$ and $\varepsilon>0$ such that every probability law on $S_4$ within total-variation distance $\varepsilon$ of uniform, and with exactly uniform coordinate marginals, satisfies the permanent bound by the product of the four $L^p$ row norms for every nonnegative matrix. The exponent and neighborhood are uniform over those laws. The Thorp mixing consequence is outside this selected statement.”
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
48 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: 18 to its own declarations, 2 to Mathlib’s documentation. 10 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 OAI/-!# A robust four-row permanent inequality`Fin 4` labels four sites. A law is represented by real probability masses;`IsProbability` asserts nonnegativity and total mass one. All row norms usethe uniform counting normalization.-/noncomputablesectionnamespace FourRowopenscoped BigOperatorsabbrevSite := Fin4abbrevPerm := Equiv.PermSiteabbrevLaw := Perm → ℝabbrevFunctions := Site → Site → ℝdefIsProbability (ν : Law) : Prop := (∀ π, 0 ≤ ν π) ∧ ∑ π, ν π = 1defUniformMarginals (ν : Law) : Prop := ∀ i j : Site, ∑ π, (if π i = j then ν π else0) = 1 / 4deftotalVariation (ν : Law) : ℝ := (∑ π, |ν π - 1 / 24|) / 2deflpNorm (p : ℝ) (f : Site → ℝ) : ℝ := ((∑ j, (f j) ^ p) / 4) ^ (1 / p)defpermanentExpectation (ν : Law) (f : Functions) : ℝ := ∑ π, ν π * ∏ i, f i (π i)theoremrobust_permanent : ∃ p₀ : ℝ, 4 / 3 < p₀ ∧ p₀ < 2 ∧ ∃ ε : ℝ, 0 < ε ∧ ∀ ν : Law,IsProbability ν → totalVariation ν < ε → UniformMarginals ν → ∀ f : Functions, (∀ i j, 0 ≤ f i j) →permanentExpectation ν f ≤ ∏ i, lpNorm p₀ (f i) := bysorryend FourRowendend 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.
As $d\to\infty$,
\[
\|q_d^{*(1600d)}-U_n\|_{\mathrm{TV}}\longrightarrow0.
\]
For every integer $t\ge0$,
\[
\|q_d^{*t}-U_n\|_{\mathrm{TV}}
\ge 1-\frac{2^{tn/2}}{n!}.
\]
Consequently the mixing time at distance $1/4$ satisfies
\[
t_{\mathrm{mix}}(d)\ge
\left\lceil\frac2n\log_2\frac{3n!}{4}\right\rceil
=2d-O(1),
\qquad t_{\mathrm{mix}}(d)=\Theta(d).
\]
Theorem(Complementary cosets control Fourier matrices)
Partition X into b nonempty sets M_1,\ldots,M_b. Put H_i=H_{M_i}. Suppose f is a nonnegative measure on G of mass at most one and, for every g\in G and i,
f(gH_i)\le \frac{B}{[G:H_i]}.
For every u>0 and every irreducible \rho of degree D,
Partition $X$ into $b$ nonempty sets $M_1,\ldots,M_b$. Put $H_i=H_{M_i}$. Suppose $f$ is a nonnegative measure on $G$ of mass at most one and, for every $g\in G$ and $i$,
\begin{equation}
f(gH_i)\le \frac{B}{[G:H_i]}.
\end{equation}
For every $u>0$ and every irreducible $\rho$ of degree $D$,
\begin{equation}
\|\widehat f(\rho)\|_{\HS}^2
\le bBC_uD^{-1+(u+2)/b}.
\end{equation}
There is also an independent orbit-span proof of the bound
\begin{equation}
\|\widehat f(\rho)\|_{\op}^2
\le bBC_uD^{-1+(u+2)/b}.
\end{equation}
No restriction is imposed on block sizes or on the multiplicities in $\rho|_{H_1\times\cdots\times H_b}$.
Theorem(Conditional permutation and full-deck mixing)
Let n=2^d, and start the shuffle from any deterministic deck. After J(s_0+1) sweeps, where s_0=400 and J=40000, the conditional free-slot law defined above satisfies
Consequently the full-deck law approaches uniform, uniformly in the starting deck, after 16\,040\,400d physical shuffles. For every integer t\ge0, after t physical shuffles the full-deck distance from uniform is at least 1-2^{tn/2}/n!. Thus the full-deck mixing time is of order d.
Let $n=2^d$, and start the shuffle from any deterministic deck.
After $J(s_0+1)$ sweeps, where $s_0=400$ and $J=40000$, the conditional free-slot law defined above satisfies
\[
\sup_{\text{initial layouts}}\E_{\mathcal E}
\|\nu_{\mathcal E}-U_{n/2}\|_{\TV}\longrightarrow0
\qquad(d\longrightarrow\infty).
\]
Consequently the full-deck law approaches uniform, uniformly in the starting deck, after $16\,040\,400d$ physical shuffles. For every integer $t\ge0$, after $t$ physical shuffles the full-deck distance from uniform is at least
$1-2^{tn/2}/n!$. Thus the full-deck mixing time is of order $d$.
There are absolute constants $\eta,c,C>0$ such that, with $a=1/1000$,
\[
\log\E_{y\sim u_{N,k}}R_x(y)^{1+a}
\le Ck(k/N)^\eta
\qquad(1\le k\le N),
\]
uniformly over every starting injection $x$. For every sufficiently
large dyadic $N$ and every nontrivial irreducible representation,
\[
T_N(\lambda)=0\quad\hbox{or}\quad
\|T_N(\lambda)\|_{\op}\le D_\lambda^{-c}.
\]
Consequently an absolute number of forward sweeps has worst-start
total-variation distance from uniform tending to zero as $d\to\infty$.
There are positive weights W_\lambda, an absolute finite p_*, and real exponents 2\le p_d\le p_* such that, for every d\ge1 and every \lambda\vdash2^d,
There are positive weights $W_\lambda$, an absolute finite $p_*$, and real
exponents $2\le p_d\le p_*$ such that, for every $d\ge1$ and every
$\lambda\vdash2^d$,
\begin{equation}
D_\lambda^{3/4}\le W_\lambda\le D_\lambda,
\qquad W_\lambda\|K_d(\lambda)\|_{p_d}^{p_d}\le1.
\end{equation}
Consequently $\|K_d(\lambda)\|_{\rm op}\le D_\lambda^{-3/(4p_*)}$.
Corollary(Optimal order of mixing)
There is an absolute constant C such that, for every d\ge1,
There is an absolute constant $C$ such that, for every $d\ge1$,
\[
\left\lceil\frac2N\log_2\frac{3N!}{4}\right\rceil
\le t_{\rm mix}(d)\le Cd,
\qquad N=2^d.
\]
In particular $t_{\rm mix}(d)=\Theta(d)$, and its lower bound is
$2d-O(1)$.
There is an absolute P\ge2 such that for every dyadic n\ge2,
\operatorname{Tr}_{\rm reg}|Q_n|^P\le1+n^{-10}.
If v is an integer with 2v\ge P, then v independent sweeps have total-variation distance at most \frac12n^{-5} from uniform, from every deterministic initial deck. Here \|\mu-\nu\|_{\rm TV}=\tfrac12\sum_g|\mu(g)-\nu(g)|.
There is an absolute $P\ge2$ such that for every dyadic $n\ge2$,
\begin{equation}
\operatorname{Tr}_{\rm reg}|Q_n|^P\le1+n^{-10}.
\end{equation}
If $v$ is an integer with $2v\ge P$, then $v$ independent sweeps have total-variation distance at most $\frac12n^{-5}$ from uniform, from every deterministic initial deck. Here $\|\mu-\nu\|_{\rm TV}=\tfrac12\sum_g|\mu(g)-\nu(g)|$.
There are absolute constants L,C_0>0 such that the following holds. Put m=\sqrt n, assume m/2\le A,D\le2m, and take m sufficiently large that \theta=1-L/\log m>0. For all nonnegative functions w_i:S_D\to[0,\infty) and v_k:S_A\to[0,\infty),
\frac{n!}{|R||C|}\,
\mathbb E_U\!\left[\mathbf1_{\mathcal I}
\prod_{i=1}^A w_i(r_i)\prod_{k=1}^D v_k(c_k)\right]
\le e^{C_0n^{.54}}
\prod_{i=1}^A(\mathbb E w_i^{1/\theta})^\theta
\prod_{k=1}^D(\mathbb E v_k^{1/\theta})^\theta .
The expectations on the right are uniform on the indicated symmetric groups, and logarithms are natural.
There are absolute constants $L,C_0>0$ such that the following holds. Put $m=\sqrt n$, assume $m/2\le A,D\le2m$, and take $m$ sufficiently large that $\theta=1-L/\log m>0$. For all nonnegative functions $w_i:S_D\to[0,\infty)$ and $v_k:S_A\to[0,\infty)$,
\begin{equation}
\frac{n!}{|R||C|}\,
\mathbb E_U\!\left[\mathbf1_{\mathcal I}
\prod_{i=1}^A w_i(r_i)\prod_{k=1}^D v_k(c_k)\right]
\le e^{C_0n^{.54}}
\prod_{i=1}^A(\mathbb E w_i^{1/\theta})^\theta
\prod_{k=1}^D(\mathbb E v_k^{1/\theta})^\theta .
\end{equation}
The expectations on the right are uniform on the indicated symmetric groups, and logarithms are natural.
Theorem(A bounded regular moment)
There is an absolute finite p_*>0 such that, for every dyadic n,
Z_n(p_*)\le1+\frac1{16}.
For every integer M\ge p_*, M independent forward sweeps have worst-start total-variation distance at most 1/8 from uniform.
There is an absolute finite $p_*>0$ such that, for every dyadic $n$,
\begin{equation}
Z_n(p_*)\le1+\frac1{16}.
\end{equation}
For every integer $M\ge p_*$, $M$ independent forward sweeps have worst-start total-variation distance at most $1/8$ from uniform.
Corollary(Singular ranks and full-deck mixing)
For every nontrivial \lambda\vdash n and 1\le r\le D_\lambda,
s_r(T_{n,\lambda})\le(16D_\lambda r)^{-1/(2p_*)}.
If s_j(T_n) denotes the full regular singular list, including all multiplicities and zeros, then
s_j(T_n)\le j^{-1/(2p_*)}\qquad(1\le j\le n!).
The constant singular value is the first value. The physical Thorp chain has worst-start 1/4 mixing time \Theta(d); more precisely its lower bound is 2d-O(1). For every fixed integer M>p_*, the worst-start total-variation distance after Md physical shuffles tends to zero as d\to\infty.
For every nontrivial $\lambda\vdash n$ and $1\le r\le D_\lambda$,
\[
s_r(T_{n,\lambda})\le(16D_\lambda r)^{-1/(2p_*)}.
\]
If $s_j(T_n)$ denotes the full regular singular list, including all multiplicities and zeros, then
\[
s_j(T_n)\le j^{-1/(2p_*)}\qquad(1\le j\le n!).
\]
The constant singular value is the first value. The physical Thorp chain has worst-start $1/4$ mixing time $\Theta(d)$; more precisely its lower bound is $2d-O(1)$. For every fixed integer $M>p_*$, the worst-start total-variation distance after $Md$ physical shuffles tends to zero as $d\to\infty$.
Theorem(A moment bound for every signed occurrence)
For each sufficiently small fixed \bar\eta>0, choose a sufficiently small \bar\kappa>0 with \bar\kappa<\bar\eta/256. There is an integer r\ge1, depending only on these fixed constants, such that for every d\ge1, every \lambda\vdash n=2^d, and every occurrence (ten:spin:eq:1),
For each sufficiently small fixed $\bar\eta>0$, choose a sufficiently
small $\bar\kappa>0$ with $\bar\kappa<\bar\eta/256$. There is an
integer $r\ge1$, depending only on these fixed constants, such that for
every $d\ge1$, every $\lambda\vdash n=2^d$, and every
occurrence~\eqref{ten:spin:eq:1},
\begin{equation}
\log\!\left(D_\lambda\operatorname{Tr}_{V_\lambda}(B_n^*B_n)^r\right)
\le\eta_d F(\alpha,\beta)+g_n(l),
\end{equation}
where
\[
\eta_d=\bar\eta\left(1-\frac1{2\sqrt d}\right),\qquad
\kappa_d=\bar\kappa\left(1-\frac1{2\sqrt d}\right),\qquad
g_n(l)=\max\{0,\kappa_d l\log n-l\log(n/l)\}.
\]
Set $g_n(0)=0$ and interpret the logarithm of a zero trace as $-\infty$.
Corollary(Fixed-sweep convergence of the full deck)
There is an absolute positive integer $L$ such that
\[
\mathbb E_{U_{S_n}}|f_{d,L}-1|^2\longrightarrow0
\qquad(d\longrightarrow\infty).
\]
The convergence is uniform over starting orders. Moreover
$t_{\mathrm{mix}}(d)=\Theta(d)$.
There are absolute constants g>0 and d_0 such that, for d\ge d_0 and every \lambda\vdash 2^d,
\|K_\lambda\|_{\op}\le D_\lambda^{-g}.
The sign representation is annihilated. There is an absolute positive integer w such that the law after w independent sweeps converges to uniform in total variation as d\to\infty, uniformly over deterministic initial decks.
There are absolute constants \(g>0\) and \(d_0\) such that, for \(d\ge d_0\)
and every \(\lambda\vdash 2^d\),
\[
\|K_\lambda\|_{\op}\le D_\lambda^{-g}.
\]
The sign representation is annihilated. There is an absolute positive integer \(w\)
such that the law after \(w\) independent sweeps converges to uniform in
total variation as \(d\to\infty\), uniformly over deterministic initial
decks.
There are absolute constants $p_0\in(4/3,2)$ and $\varepsilon>0$ with the following property. Let $\nu$ be a probability measure on $S_4$ such that
\[
\|\nu-u\|_{\TV}<\varepsilon,
\qquad
\nu\{\pi:\pi(i)=j\}=\frac14\quad(1\le i,j\le4).
\]
Then every four nonnegative functions on $\{1,2,3,4\}$ satisfy
\begin{equation}
\E_\nu\prod_{i=1}^4f_i(\pi(i))\le\prod_{i=1}^4\|f_i\|_{p_0}.
\end{equation}
Theorem(A fixed moment with a degree saving)
There exist an even integer r\ge2 and \eta>0, independent of N, such that for every dyadic N and every \lambda\vdash N,
Consequently there is an absolute integer q for which the total-variation distance of q independent forward sweeps from uniform tends to zero as N\to\infty, uniformly over the starting deck.
There exist an even integer $r\ge2$ and $\eta>0$, independent of $N$, such that for every dyadic $N$ and every $\lambda\vdash N$,
\begin{equation}
D_\lambda\Tr B_N(\lambda)^r\le D_\lambda^{1-\eta}.
\end{equation}
Consequently there is an absolute integer $q$ for which the total-variation distance of $q$ independent forward sweeps from uniform tends to zero as $N\to\infty$, uniformly over the starting deck.
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.
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