Alphabeta Math
Pipeline-generated
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.

Conditional entropy chain and disjoint-block bounds — Examples

1 · Prerequisites

2 · Summary

These finite Bernoulli countermodels separate energy-density or relative-entropy density agreement from convergence at a selected coordinate. They compute the first-coordinate total-variation discrepancy and the conditional relative entropy exactly, then verify concentration and entropy-density limits directly. They are mathematical counterexamples, not experimental observations or claims about a physical ensemble.

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

None yet.

5 · Examples, experiments and counterexamples

CounterexampleConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-6-luna)Open item page →

Energy concentration without local equivalence

Statement refuted

Fix 0<θ<1 and ϵ>0. For every integer N≥1, let PN=Bernoulli⁡(θ)⊗N be the product probability on {0,1}N, where each coordinate equals 1 with probability θ, and define HN(x)=ϵ∑i=1Nxi. The refuted claim is: every sequence of probability laws AN on {0,1}N for which there are deterministic numbers eN→ϵθ with AN(HN/N=eN)=1 has first-coordinate marginals converging in event-supremum total variation to PN(1)=Bernoulli⁡(θ). Use ∥P−Q∥TV=sup⁡A∣P(A)−Q(A)∣. This is a finite-model claim, not an experimental assertion.

Facts & Assumptions

Given: 0<θ<1, ε>0, the Bernoulli product reference, and the configurations above.

Counterexample

Given: 0<θ<1, ϵ>0, the independent Bernoulli-θ reference PN, and HN(x)=ϵ∑ixi.

1.1givenconstructalgebra

Let x(N) have its first ⌊θN⌋ coordinates equal to one and the rest zero, and set AN=δx(N). Then HN(x(N))/N=ϵ⌊θN⌋/N→ϵθ, since 0≤θN−⌊θN⌋<1. For all sufficiently large N, the first coordinate of AN is one.

2.1step 1.1algebra∎

The event {1} in the first-coordinate space has probability difference 1−θ, so ∥AN(1)−PN(1)∥TV≥1−θ>0 for every sufficiently large N. In fact equality holds because on a two-point space the event-supremum difference between δ1 and Bernoulli-θ is 1−θ. Together with the deterministic energy limit in step 1.1, this refutes the stated implication.

CounterexampleConstruction: AI-adaptedVerification: AI-adaptedOpen item page →

Small entropy density without homogeneous local convergence

Statement refuted

Assume AC. Fix 0<θ<1 and ϵ>0, and let PN,HN be the reference and energy of Energy concentration without local equivalence. The refuted implication is: for every sequence of laws QN≪PN, if N−1D(QN∥PN)→0, HN/N→ϵθ in QN-probability, and N−1HSh(QN)→h(θ), then the first-coordinate marginal of QN converges in event-supremum total variation to Bernoulli(θ). Here HSh(QN)=−∑xQN(x)log⁡QN(x) and h(θ)=−θlog⁡θ−(1−θ)log⁡(1−θ) use natural logarithms.

Facts & Assumptions

Given: AC, the Bernoulli model and parameters above.

[A1]

AC is the choice hypothesis of the conditional entropy chain supplier (The Axiom of Choice).

[F1]

Conditional entropy chain and disjoint-block bounds: the finite product KL chain rule separates the first coordinate from its conditional remainder.

Counterexample

Given: The finite product laws and energy.

1.1A1F1givenalgebra

Set AN:=δ1⊗Bernoulli⁡(θ)⊗(N−1), using the empty product for N=1. Under AN, X1=1 surely and the remaining coordinates retain their independent Bernoulli-θ law. The likelihood dAN/dPN is 1/θ on {X1=1} and zero elsewhere, so integration gives D(AN∥PN)=log⁡(1/θ). Equivalently, [F1] decomposes the divergence into D(δ1∥Bernoulli(θ))=log⁡(1/θ) and a zero conditional remainder. Dividing by N gives vanishing entropy density.

2.1step 1.1algebra∎

Under AN, HN=ϵ(1+YN) with YN binomial (N−1,θ). Since YN is the sum of N−1 independent Bernoulli coordinates, EYN=(N−1)θ and Var⁡(YN)=(N−1)θ(1−θ), by expanding the mean and variance and using zero cross-covariances. Thus E(HN/N)=ϵ[1+(N−1)θ]/N→ϵθ and Var⁡(HN/N)=ϵ2(N−1)θ(1−θ)/N2→0. For any η>0, 1{∣HN/N−E(HN/N)∣≥η}≤(HN/N−E(HN/N))2/η2; integrating gives the Chebyshev bound and convergence in probability. Its Shannon entropy is HSh(AN)=(N−1)h(θ) by adding the logarithms of the independent-coordinate probabilities, so HSh(AN)/N=(N−1)h(θ)/N→h(θ), where h(θ)=−θlog⁡θ−(1−θ)log⁡(1−θ). Yet the first coordinate remains one and is at total-variation distance 1−θ from Bernoulli(θ). This proves all claims and refutes the statement.

Sources