Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-10
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Uniform laws on expanding finite grids converge to uniform zero one

Example

Assume AC. For n1, the laws μn=n1k=1nδk/n converge weakly to Lebesgue probability on [0,1].

Facts & Assumptions

[F1]

The Axiom of Countable Choice (ACω): The Axiom of Countable Choice, written ACω, is the following statement.

For every family (Xn)nN of nonempty sets indexed by N there is a function f with domain N such that f(n)Xn for every nN.

Equivalently, in the vocabulary of def-choice-function: every at most countable family of nonempty sets (def-countable) has a choice function.

[F2]

A box in Rn with parameters aibi is Lebesgue measurable of measure i<n(biai), whichever of its faces are included: Let n1, assume the Axiom of Countable Choice (def-countable-choice), and let aibi be reals for i<n. Write

R:={xRn:ai<xi<bi for every i<n},R:=[a,b]={xRn:aixibi for every i<n}

(def-multidimensional-rectangle-and-volume). Then R is open and R is closed, so both are Borel and Lebesgue measurable, and every set R with RRR is Lebesgue measurable with

λn(R)  =  i<n(biai).

In particular this covers the four one-dimensional face conventions in each coordinate — the open box, the closed box [a,b], the half-open box B(a,b)=i<n(ai,bi] of def-half-open-box, and every mixture of them, in any combination of coordinates — and it gives measure 0 to all of them whenever ai=bi for some i<n. For a half-open box with infinite parameters the value is already λn(B)=vol(B) (thm-lebesgue-measure-is-a-complete-measure).

[F3]

The nonnegative integral agrees with the simple integral on simple functions: If s is a nonnegative simple measurable function, then its nonnegative Lebesgue integral equals its simple integral: sdμ=simplesdμ.

[F4]

Heine-Cantor in R: a continuous real function on a compact subset of R is uniformly continuous, proved R-natively from sequential compactness: Let KR be compact (def-open-cover-r) and let f:KR be continuous on K (def-continuity-real). Then f is uniformly continuous on K (def-uniform-continuity-real).

This theorem is stated twice in this library, on purpose. Its metric-space twin is thm-heine-cantor-metric, proved there from the cover machinery of metric spaces; the proof below is R-native and runs through thm-compact-iff-sequentially-compact-r, which is order-based. That the two statements are the same statement in two vocabularies is lem-real-and-metric-notions-agree, clauses 1, 2 and 5, immediately above.

The choice cost, named. The proof invokes the axiom of countable choice (def-countable-choice) exactly once, at step 3.1, to select one bad pair of points from each of countably many nonempty sets. The backward implication of thm-compact-iff-sequentially-compact-r also spends countable choice, and that item names its own uses; the forward implication used here, from compact to sequentially compact, does not. No claim is made that the axiom is necessary for either.

[F5]

The Lebesgue integral is linear on L1(μ): The class L1(μ) is a complex vector space, and the Lebesgue integral is complex-linear on it: (αf+βg)dμ=αfdμ+βgdμ(α,βC, f,gL1(μ)).

[F6]

The modulus of an integral is bounded by the integral of the modulus: If fL1(μ), then fdμfdμ.

[F7]

Weak convergence of borel probability measures: For Borel probability measures μn,μ on a metric space S, write μnμ if fdμnfdμ for every bounded continuous real function f on S. Continuity is def-metric-continuity. Such f is Borel measurable (inverse images of open sets are open) and fdμfμ(S)<, so the integrals are finite in def-integrable-real-and-complex-functions-and-their-integrals. No completeness or coupling is required.

Verification

Given: The objects, hypotheses and definitions in the statement. Its conclusions are to be established below.

1.1

Fix n1. AC restricted to a countable family gives F1. By F2, the Borel restriction lambda on [0,1] has mass one, each interval ((k-1)/n,k/n] has mass 1/n, and {0} has mass zero. The finite sum defining μn is a probability: disjoint-set indicators add at each of its n atoms and total mass is n/n=1.

F1F2
2.1

For a bounded continuous real f, put sn(x)=f(k/n) on ((k-1)/n,k/n] and sn(0)=f(0). By F3 applied to positive and negative parts, sndλ=n1k=1nf(k/n)=fdμn. F4, with its CC use supplied by step 1.1, makes f uniformly continuous on [0,1]. Thus snf0, since each cell has length 1/n.

F3F4step 1.1
3.1

F5 and F6 give fdμnfdλ=(snf)dλsnfλ([0,1])0. This is F7.

F5F6F7

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

52 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources