`ComparatorChallenges/LogConcaveQuery.lean`

Explanation legend Lean4 Domain Explanation Language (L4DEL)

L4DEL explanations are a separate layer. They do not change the Lean source. Intuition is explanatory prose; numbered items are a reading path; sources link the wording back to evidence.

L4DEL: Lean4 Domain Explanation Language

Annotation provenance. This overlay contains 120 curated AI-written annotations and 1273 mechanical source-derived annotations. They have not been human-reviewed as explanations; treat them as unverified and not mathematical authority. Retained generation history: The 9 original curated annotations were produced in a Claude Sonnet 5.5 session at medium effort; Claude Opus 5.5 is recorded in that session as the advisor model, not the direct author. Those 9 annotations were revised on 2026-10-14 to bind all of them to exact page-one quotations from 6 pinned OpenAI manuscripts, the matching pinned formalization notes, and named historical primary sources; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. The 3 manuscript-grounded matrix-multiplication annotations were composed from pinned OpenAI manuscripts and named historical sources in a Codex session on 2026-10-14; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. Six additional target-specific annotations for Catalan's constant, the irrationality exponent of pi, dyadic Erdos similarity, Unique Games, quintic Lienard systems, and Yau nodal growth were composed from pinned OpenAI manuscripts, exact page-one quotations, exact Lean statements, and named historical sources in a Codex session on 2026-10-14; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. Four additional target-specific annotations for Seymour's second-neighborhood conjecture, Cannon's conjecture, Koebe circle-domain uniformization, and circle-domain rigidity were composed from 4 pinned OpenAI manuscripts, exact page-one quotations, exact Lean statements, and named historical primary sources in a Codex session on 2026-10-14; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. Four additional target-specific annotations for simple Lebesgue spectrum on the three-torus, Rokhlin multiple mixing, the nine-dimensional Borsuk counterexample, and Tingley's sphere-isometry problem were composed from 4 pinned OpenAI manuscripts, exact page-one quotations, exact Lean statements, and named historical primary sources in a Codex session on 2026-10-14; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. Four additional target-specific annotations for the Abhyankar-Sathaye counterexample, the circulant Hadamard conjecture, the Eilenberg-Ganea counterexample, and short Egyptian fractions were composed from 4 pinned OpenAI manuscripts, exact page-one quotations, exact Lean statements, and quotations from Sathaye, Turyn with Storer, Eilenberg with Ganea, and Erdos in a Codex session on 2026-10-14; no human review is claimed, and the exact runtime model identifier was not exposed to the artifact generator. Four additional target-specific annotations for the complete Crouzeix inequality, unrestricted Euclidean five-coloring, the general Mahler conjecture, and Thompson F nonamenability were composed on 2026-10-14 from exact Lean statements, 4 pinned OpenAI manuscripts, the matching formalization notes, and linked historical sources by Crouzeix, de Grey, Mahler, Cannon, Floyd, and Parry; no human review is claimed. Four additional target-specific annotations for algorithmic strong thin trees, Barnette's Hamiltonian-cycle conjecture, the explicit Artin CAT(0) counterexample, and generalized amplitude-damping capacity were composed on 2026-10-14 from exact Lean statements, 4 pinned OpenAI manuscripts, matching formalization notes, and linked historical sources by Anari with Goddyn, Arts with Hopman and Timmermans, Charney, and Khatri with Sharma and Wilde; no human review is claimed. Fourteen solution-level annotations for Seymour's second-neighborhood theorem were added on 2026-10-14 from the pinned 15-page manuscript, covering the exact-neighborhood model, minimal-counterexample reduction, subset deficit, transitive blowup, pruning, extremal contradiction, and proof-bearing final theorem. The subset and blowup annotations also bind exact quotations from Tyler Seacrest (2019) and Brantner, Brockman, Kay, and Snively (2009); no human review is claimed. Thirty-seven declaration-level annotations were added on 2026-10-14 for the rest of ComparatorChallenges/AlgorithmicThinTrees.lean, covering its multigraph semantics, stack-machine cost model, explicit and binary-multiplicity encodings, output certificate, and complete algorithmic proposition. They are bound to the pinned OpenAI manuscript and linked earlier thin-tree sources by Luis Goddyn, Nima Anari, and Shayan Oveis Gharan; the annotation explicitly preserves that the concluding comparator theorem still has body by sorry, and no human review is claimed. Thirty-one supporting declarations in ComparatorChallenges/UniqueGamesTheorem.lean were upgraded or added on 2026-10-14, completing curated coverage of all thirty-two declarations in that file. The annotations cover the finite game model, Boolean formula and parser, binary-vector translations, explicit output serialization, finite-alphabet machine contract, and gap theorem; they distinguish manuscript-backed mathematics from Lean-specific canonical parsing and unary game output encoding, bind exact OpenAI manuscript quotations, and credit Subhash Khot through his 2010 survey. The comparator theorem remains explicitly labeled as having body by sorry; no human review is claimed. The source-derived generator was hardened on 2026-10-14 so a formalization note covering multiple comparator results no longer copies its combined Scope prose onto each declaration; those targets now use declaration-specific source comments when available and otherwise remain explicitly bounded to the exact Lean statement. The remaining source-derived annotations were generated mechanically from pinned formalization notes and source comments by tooling developed in a GPT-5.6 Sol session at ultra reasoning; GPT-5.6 Sol did not originate the source prose.

Credit: OpenAI's pinned repository says an unreleased internal model produced the manuscripts and supporting proof artifacts. The collection builds on the mathematics community's accumulated problems, results, language, and formal libraries; each manuscript's BibTeX and bibliography are the authority for individual scholarly credit.

Project sources: Pinned openai/math documentation, Pinned formalization catalogue

17 declarations, 1 contain sorry, 0 declared as axioms

abbrev OAI.LogConcaveSampling.Point line 12
Also declared: OAI/Probability/LogConcave/Model.lean:16 (same declaration text).
abbrev OAI.LogConcaveSampling.Reply line 13
Also declared: OAI/Probability/LogConcave/Model.lean:17 (same declaration text).
abbrev OAI.LogConcaveSampling.Transcript line 14
Also declared: OAI/Probability/LogConcave/Model.lean:18 (same declaration text).
structure OAI.LogConcaveSampling.Admissible Normalized twice continuously differentiable potentials with Hessian between the identity and twice the identity line 17
Source documentation
Normalized twice continuously differentiable potentials with Hessian between the identity and twice the identity.
L4DEL explanation
Normalized twice continuously differentiable potentials with Hessian between the identity and twice the identity.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:21 (same declaration text).
```
structure Admissible {d : ℕ} (V : Point d → ℝ) : Prop where
…
```
Mentions: OAI.LogConcaveSampling.Point
def OAI.LogConcaveSampling.gibbsDensity The unnormalized Gibbs density relative to Euclidean volume line 26
Source documentation
The unnormalized Gibbs density relative to Euclidean volume.
L4DEL explanation
The unnormalized Gibbs density relative to Euclidean volume.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:30 (same declaration text).
```
def gibbsDensity {d : ℕ} (V : Point d → ℝ) (x : Point d) : ℝ≥0∞ :=
…
```
Mentions: OAI.LogConcaveSampling.Point
def OAI.LogConcaveSampling.partition line 29
Also declared: OAI/Probability/LogConcave/Model.lean:33 (same declaration text).
def OAI.LogConcaveSampling.gibbs The Gibbs probability law, normalized by its partition function line 33
Source documentation
The Gibbs probability law, normalized by its partition function.
L4DEL explanation
The Gibbs probability law, normalized by its partition function.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:37 (same declaration text).
```
def gibbs {d : ℕ} (V : Point d → ℝ) : Measure (Point d) :=
…
```
Mentions: OAI.LogConcaveSampling.Point
def OAI.LogConcaveSampling.TVAtMost line 36
Also declared: OAI/Probability/LogConcave/Model.lean:40 (same declaration text).
def OAI.LogConcaveSampling.firstOrderReply line 40
Also declared: OAI/Probability/LogConcave/Model.lean:44 (same declaration text).
structure OAI.LogConcaveSampling.OracleAlgorithm Measurable adaptive queries and output, parametrized by private randomness line 44
Source documentation
Measurable adaptive queries and output, parametrized by private randomness.
L4DEL explanation
Measurable adaptive queries and output, parametrized by private randomness.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:48 (same declaration text).
```
structure OracleAlgorithm (Ω : Type*) [MeasurableSpace Ω] (d q : ℕ) where
…
```
def OAI.LogConcaveSampling.OracleAlgorithm.history line 50
Also declared: OAI/Probability/LogConcave/Model.lean:54 (same declaration text).
def OAI.LogConcaveSampling.OracleAlgorithm.run line 59
Also declared: OAI/Probability/LogConcave/Model.lean:63 (same declaration text).
def OAI.LogConcaveSampling.CanSample A uniform sampler with a deterministic query budget on every execution and total-variation error at most one tenth line 64
Source documentation
A uniform sampler with a deterministic query budget on every execution and total-variation error at most one tenth.
L4DEL explanation
A uniform sampler with a deterministic query budget on every execution and total-variation error at most one tenth.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:68 (same declaration text).
```
def CanSample (d q : ℕ) : Prop :=
…
```
def OAI.LogConcaveSampling.queryComplexity The smallest feasible integer query budget, or infinity when no budget is feasible line 71
Source documentation
The smallest feasible integer query budget, or infinity when no budget is feasible.
L4DEL explanation
The smallest feasible integer query budget, or infinity when no budget is feasible.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:75 (same declaration text).
```
def queryComplexity (d : ℕ) : ℕ∞ :=
…
```
def OAI.LogConcaveSampling.ExponentBound The quantified dimension-exponent upper bound with every positive exponent slack line 75
Source documentation
The quantified dimension-exponent upper bound with every positive exponent slack.
L4DEL explanation
The quantified dimension-exponent upper bound with every positive exponent slack.
1. This explanation is a condensed rendering of the declaration's own doc comment at the pinned source commit; consult the exact Lean statement below for authority.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Also declared: OAI/Probability/LogConcave/Model.lean:79 (same declaration text).
```
def ExponentBound (γ : ℝ) : Prop :=
…
```
def OAI.LogConcaveSampling.gammaStar line 79
Also declared: OAI/Probability/LogConcave/Model.lean:83 (same declaration text).
theorem OAI.LogConcaveSampling.exact_source_main Challenge statement: Subpolynomial query complexity for log-concave sampling sorry line 81
L4DEL explanation
The formalized result determines the dimension exponent of exact value-and-gradient query complexity for well-conditioned log-concave sampling. For potentials with $V(0)=0$, $\nabla V(0)=0$, and $I\le\nabla^2V\le2I$, the least worst-case query budget achieving total-variation error at most $1/10$ is at most $C_\varepsilon d^\varepsilon$ for every $\varepsilon>0$, and at least $c\log d$ eventually. Hence the infimal polynomial exponent is zero. Algorithms may be measurable, randomized, and adaptive; arithmetic and bit costs are not bounded.
1. This is an exact comparator target linked by the pinned repository's formalization note for this result.
2. The repository's comparator configuration selects OAI.Probability.LogConcave.Main as the released solution module checked against this declaration.
Sources and attribution: Pinned Lean source (fd4aeeb2ee4f)
Distinct definitions of this name: OAI/Probability/LogConcave/Main.lean:68 (same statement text; different proof or body). Qualify references as OAI.LogConcaveSampling.exact_source_main@path.
```
theorem exact_source_main :
    (∀ ε : ℝ, 0 < ε → ∃ C : ℝ, ∀ d : ℕ, 2 ≤ d →
      (queryComplexity d).toENNReal ≤ ENNReal.ofReal (C * (d : ℝ) ^ ε)) ∧
    (∃ c : ℝ, 0 < c ∧ ∃ d₀ : ℕ, 2 ≤ d₀ ∧ ∀ d : ℕ, d₀ ≤ d →
      ENNReal.ofReal (c * Real.log (d : ℝ)) ≤ (queryComplexity d).toENNReal) ∧
    gammaStar = 0 := by
…
```
Mentions: OAI.LogConcaveSampling.queryComplexity, OAI.LogConcaveSampling.gammaStar