Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)
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.

Nonunique minimizers allow distinct local accumulation limits

Example

Suppose J(P)=I(P)+βΦ(P)−ρ(P)log⁡z has two distinct stationary minimizers P0≠P1 with equal finite value m. Every finite mixture Pθ=θP0+(1−θ)P1, 0≤θ≤1, also minimizes J. The sequence of stationary laws alternating between P0 and P1 has two distinct local accumulation points and does not converge. This conditional mathematical example shows why compactness alone yields minimizer-set accumulation; it does not assert a finite-volume ensemble realizes the sequence or that a particular continuum potential exhibits coexistence.

Facts & Assumptions

Given: Two distinct stationary minimizers P0,P1 with finite equal minimum m.

[F1]

Superstable regular continuum grand pressure and variational formula: Φ(P) is the limit of normalized finite-volume expected energies, and ρ(P) is expected unit-cell count.

[F2]

Specific Relative Entropy Density: I(P) is the large-cube limit of relative entropies against the unit-intensity Poisson reference.

[F3]

Relative Entropy Variational, Chain, and Conditioning Rules: relative entropy obeys the mixture-label chain rule and is nonnegative.

[F4]

Local-Tame Topology: τL tests every bounded measurable local observable.

Verification

Given: The assumptions above.

1.1F1F2F3algebra

Let P,R be stationary laws of finite specific entropy and let Pθ=θP+(1−θ)R. For a bounded cube G, the binary-label chain rule [F3] gives θD(PG∥QG)+(1−θ)D(RG∥QG)=D((Pθ)G∥QG)+I(S;XG), where S is the mixture label and XG the observed configuration. Since 0≤I(S;XG)≤H(S)<∞, divide by ∣G∣ and take the cube limit [F2]; H(S)/∣G∣→0, so I(Pθ)=θI(P)+(1−θ)I(R). The expectation defining ρ is affine. For finite energy densities, EPθHG=θEPHG+(1−θ)ERHG for every G; dividing by volume and taking limits gives affinity of Φ. Thus J is affine on finite mixtures of finite-J laws.

2.1givenF4step 1.1algebra∎

Applying step 1.1 to P0,P1 gives J(Pθ)=θJ(P0)+(1−θ)J(P1)=m, so every mixture is a minimizer. Put P2n=P0 and P2n+1=P1. The even and odd subsequences converge locally to P0 and P1. Since distinct laws differ on a bounded-region measurable event, its indicator is a bounded local test by [F4] with different expectations; the full sequence therefore has no τL limit. Both accumulation points are minimizers.

Depends on

Dependency tree · 2 levels

Sources