OpenAI groups related papers into a “family”. OpenAI’s catalogue describes this one:
Determines the optimal worst-case covering density as \(\Theta(n\log n)\), for both lattice and unrestricted translative coverings. Every convex body in ℝn, n ≥ 2, admits a lattice covering of density at most \(Cn\log n\); centrally symmetric examples in every sufficiently large dimension require at least \(cn\log n\) even without the lattice restriction, for absolute \(c,C\gt 0\). (OpenAI’s Lean note)
Lean is a language for mathematical statements and proofs that a computer can verify. The assessments below ask whether those statements express the result OpenAI describes here.
Both independent assessments find that the Lean statements express or imply the whole result described above.Read the assessments
Possible match to OpenAI’s description, selected from the papers’ main statements because it shares the most wording. No review confirming the selection is recorded.
There are absolute constants c>0 and n_0 such that, for every integer n\ge n_0, some centrally symmetric convex body K_n\subset\mathbb R^n satisfies
The paper’s main theorem is shown as a possible match to OpenAI’s description. No review confirming the selection is recorded.
There is an absolute constant C>0 such that, for every integer n\ge2 and every convex body K\subset\R^n, one full-rank lattice \Lambda satisfies
K+\Lambda=\R^n,\qquad
\frac{\vol_n(K)}{\det\Lambda}\le Cn\log n.
No symmetry or boundary regularity is assumed.
The release does not say which paper theorem its catalogue describes. Both statements above are shown whole, the closer match to the description’s wording first. The atlas has not reviewed either choice.
Reported proof verification
Two Lean statements in the release; independent groups report that Comparator accepted runs for all these files at the version shown here.
Every convex body in ℝn, n ≥ 2, admits a covering by translates along one full-rank lattice with density at most \(Cn\log n\), for an absolute constant C. No symmetry or boundary regularity is assumed.
For every sufficiently large dimension n, we construct a centrally symmetric convex body whose translative covering density exceeds \(c n\log n\), where c > 0 is absolute. This disproves the existence of a universal linear upper bound and matches the order of Rogers’ upper bound.
Two Lean statements in this family
Choose one to see it beside the paper theorem it is paired with.
For a configured theorem, the Comparator program tests whether OpenAI’s separate proof proves the exact Lean statement using only the allowed axioms. Some statement files instead configure definitions for comparison by name and type. Acceptance does not establish that a formal statement means the same thing as the paper’s theorem.
SingleLatticeCovering.lean, the release lists it as a main result.
theoremsingle_lattice_covering : ∃ C : ℝ, 0 < C ∧ ∀ (n : ℕ), 2 ≤ n → ∀ (K : Set (Space n)), IsConvexBody K → ∃ (L : Submodule ℤ (Space n)) (_ : DiscreteTopology L), IsZLattice ℝ L ∧ LatticeCovers K L ∧ (volume K).toReal / ZLattice.covolume L ≤ C * (n : ℝ) * Real.log (n : ℝ) := bysorry
This file states the theorem with sorry, a placeholder, in place of its proof. OpenAI supplies a separate proof file. Comparator tests whether that proof proves the statement here.
three independent groups report that Comparator accepted runs for this Lean statement file: cert-machine; MathVet; Neal McBurnett, openai-math-lean-tooling. Who ran the proof-verification program
cert-machine records it as “CERTIFIED”.
MathVet records it as “comparator-sandboxed-pass”.
Neal McBurnett, openai-math-lean-tooling records it as “accepted”.
CoveringDensity.lean, the release lists it as a supporting statement.
defOptimalOrder : Prop := ∃ c C : ℝ, 0 < c ∧ 0 < C ∧ ∃ n₀ : ℕ, ∀ n : ℕ, n₀ ≤ n → orderBounds c C n (translativeSup n) ∧ orderBounds c C n (symmetricTranslativeSup n) ∧ orderBounds c C n (latticeSup n) ∧ orderBounds c C n (symmetricLatticeSup n)⋮theoremCoveringOrder.optimal_order : CoveringOrder.OptimalOrder := bysorry
This file states the theorem with sorry, a placeholder, in place of its proof. OpenAI supplies a separate proof file. Comparator tests whether that proof proves the statement here.
Other mathematical statements in OpenAI’s Lean library
Atlas found these propositions by scanning the text of the project’s Model.lean file. Each name links to its definition, with any associated statement file beside it. This list does not record running Lean to interpret or verify those definitions.
Independent comparisons and proof-verification reports
Does the Lean express the advertised result?
MathVet and cert-machine agree on how much of the advertised result the Lean statements express.
MathVet and Carlos Toledo’s cert-machine independently assessed whether this family’s Lean statements express the whole result OpenAI describes in its catalogue. These are assessments of the family as a whole, not of each paper theorem separately.
MathVet finds that the Lean statements express or imply the whole advertised result (their term: “full”).
Their note:None; the lower bound for centrally symmetric bodies is stated for the translative supremum too (hence for the lattice one).
Definitions the group asks readers to compare with the paper:Hand-rolled density notions: euclideanVolume (pushforward of product Lebesgue measure), euclideanCovolume, thetaL, thetaT with upperCenterIntensity := limsup #(X ∩ cube R) / (2R)^n; standard in form
“full”, in their words: The challenge statements imply the headline claim after unpacking definitions or one routine step.
Their method: 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.
The formalized result gives an absolute constant $C>0$ such that every convex body $K\subset\mathbb R^n$, $n\ge2$, admits a covering by translates along one full-rank lattice $L$ with density $|K|/\mathrm{covol}(L)\le Cn\log n$. The covering is exact, and no symmetry, boundary regularity, or volume normalization is assumed.
The formalization determines the order of the largest covering density in dimension $n$. For all sufficiently large $n$, the suprema of translative covering density and lattice covering density are each bounded above and below by absolute positive multiples of $n\log n$, both over all convex bodies and over centrally symmetric convex bodies.
AI-drafted comparisons, with no recorded human review
Unreviewed AI draft: states a paper’s theorem as the paper does. States the main theorem: every convex body in dimension n ≥ 2 is covered by translates along one lattice with density at most Cn log n.
Optimal order for translative and lattice covering-density supremaCoveringDensity
Unreviewed AI draft: states a paper’s theorem as the paper does. States matching upper and lower bounds of order n log n for the translative and lattice covering-density suprema, over all and over centrally symmetric bodies. Its lower bound contains the main theorem of the translative paper.
Who ran the proof-verification program?
These groups report running Comparator with the release’s Lean statement files. For a configured theorem, it verifies OpenAI’s separate proof against the exact statement; some files instead configure definitions for comparison by name and type. Each row is the named group’s report. Atlas has not repeated the runs.
These reports do not list the software versions, proof-verification programs, axioms, time or memory in separate fields. Any such details in a report’s prose are reproduced below.
What these papers cite in the release, and what cites them
These links come from the papers’ bibliographies. A citation records that one paper cites another; how each cited result is used has not been examined here.
Papers elsewhere in the release that this family’s papers cite
This family’s papers cite no paper elsewhere in the release.
Papers elsewhere in the release that cite this family’s papers
No paper elsewhere in the release cites this family’s papers.
Follow-up work by others
Hexagon, Hexagon: a repository for research in the mathematical sciences and theoretical computer science, 8 October 2026. Source
hexagon:2610.00165v1 (Scott Kominers) constructs multi-coverings by lattice translates building on OpenAI’s horizontal-vertical covering construction; the family is probably 092 (convex-body covering density) but the abstract does not give an id.
References outside the release
Each paper’s outside references are listed with it on its reading page, where each citation is placed. Every outside work cited in the release: References outside the release
Explain a result, compare statements or propose a correction
Atlas has no contributed explanation for this family yet. You can offer one with a GitHub account. You receive named credit and disclose any AI assistance.
Each button opens a GitHub form with this page and its source version filled in. Opening the form does not reserve the work.
Commentary on this family by others, dated and credited. It is commentary, not a finding.
An unsigned public tier list, the OpenAI Math Tier List, classes it as a proof and rates its significance, if it holds up, as “Major math journal”. The page says that nothing on it is verified.
Each item identifies a statement, definition or report that needs further review. Its link opens a GitHub form with the source version already recorded.
Read the Lean statement against the paper
An AI agent drafted an assessment of which part of the paper this Lean statement expresses. No named human review is recorded.Open the form
An AI agent drafted an assessment of which part of the paper this Lean statement expresses. No named human review is recorded.Open the form
Identify the theorem the catalogue describes
Family 092The theorem shown was selected from shared wording with OpenAI’s description. Atlas records no review confirming that it is the theorem the description means.Open the form
Address a question raised in an independent assessment
The challenge statements imply the headline claim after unpacking definitions or one routine step.
Their method
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.
The challenge statements state the headline as OpenAI wrote it.
Their method
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…
Re-run through Comparator in a sandboxed virtual machine: accepted.
Date
8 October 2026
Version
at the release’s recorded version
Their method
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.
Re-run through Comparator with Lean’s kernel: accepted (the check took 227 s; peak memory 6.6 GiB).
Date
11 October 2026
Version
at the release’s recorded version
Their method
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.
Re-run through Comparator in a sandboxed virtual machine: accepted.
Date
8 October 2026
Version
at the release’s recorded version
Their method
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.
three independent groups report that Comparator accepted runs for this Lean statement file: cert-machine; MathVet; Neal McBurnett, openai-math-lean-tooling.