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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.

Spatial Measure And Information — Examples

1 · Prerequisites

2 · Summary

These examples verify the zero-information reference baseline, compute the specific relative entropy of a Poisson law with changed intensity, and exhibit a local superlinear count observable excluded from the local-tame test class. They use only the mathematical configuration and information definitions on the A page; no physical preparation or observation is asserted. The B inventory is 3 items (cap 8).

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

None yet.

5 · Examples, experiments and counterexamples

ExampleConstruction: AI-generatedVerification: AI-generatedverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)Open item page →

The Reference Has Zero Specific Relative Entropy

Example

Take P=Q, the unit-intensity Poisson configuration reference. This gives the zero-information baseline for the A-page specific relative entropy.

Facts & Assumptions

Given: P=Q as probability laws on the configuration space.

[F1]

Finite-region relative entropy is D(PG∥QG)=∫hGlog⁡hG dQG when hG=dPG/dQG (Relative Entropy of Probability Measures).

[F2]

Specific relative entropy is the large-cube limit of normalized finite-region relative entropies (Specific Relative Entropy Density).

[F3]

Poisson Reference Counts and Translation Invariance: under the declared AC hypothesis, Q=Q1 is stationary and has expected unit-cell count one, so it belongs to the stationary finite-intensity domain of [F2].

Verification

Given: P=Q.

1.1givenF1F3algebra

By [F3], P=Q satisfies the stationary finite-intensity hypotheses of [F2]. For every bounded cube G, equality of the laws gives PG=QG and the Radon–Nikodym derivative hG=1 QG-almost everywhere. Thus D(PG∥QG)=∫1log⁡1 dQG=0.

2.1F2step 1.1∎

Every normalized cube entropy is therefore zero, so its limit is I(P)=0 by [F2]. Also ∣G∣−1log⁡hG=0 P-almost surely for every such G.

ExampleConstruction: AI-generatedVerification: AI-generatedverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)Open item page →

Specific Relative Entropy of a Poisson Law with Changed Intensity

Example

For intensity z>0, let P=Qz be the translation-invariant Poisson law from Poisson Configuration Reference, compared with the unit-intensity reference Q=Q1. Its specific relative entropy is I(Qz)=1−z+zlog⁡z. The parameter z is a mathematical intensity relative to the chosen reference; no physical preparation is asserted.

Facts & Assumptions

Given: AC, d≥1, z>0, and P=Qz with reference Q=Q1.

[F1]

The law Qλ is invariant under every translation, its cell count is Poisson with mean λ, conditional positions have the same uniform law for all λ, and disjoint cells are independent (Poisson Configuration Reference, Poisson Reference Counts and Translation Invariance).

[F2]

Finite-region relative entropy is computed from a Radon–Nikodym density, and the specific entropy is the normalized cube limit (Relative Entropy of Probability Measures, Specific Relative Entropy Density).

[F4]

The class Pstat1(Ω) consists exactly of laws invariant under every Tx with finite EPNC0. Such laws integrate every local tame observable (Local-Tame Topology).

Verification

Given: The hypotheses and the laws in Facts & Assumptions.

1.1F1algebra

In one unit cell, the conditional distribution of point positions given the count is identical for Qz and Q1. The ratio of their count probabilities at n is e−zzn/n!e−1/n!=e1−zzn. Thus this is the Radon–Nikodym density on that cell.

2.1F1step 1.1algebra

The cube Λm=[0,m)d is the disjoint union of md unit cells. Independence in [F1] makes the finite-cube density the product of the cell densities: hm(ω)=e(1−z)mdzNΛm(ω).

3.1F1F2F3step 2.1algebra

The count NΛm is Poisson with mean zmd by [F1]. Directly from its mass function and the exponential series, EPNΛm=e−zmd∑n≥1n(zmd)nn!=zmde−zmd∑k≥0(zmd)kk!=zmd. Therefore [F2] gives D(PΛm∥QΛm)md=EPlog⁡hmmd=1−z+zlog⁡z for every positive integer m.

4.1F1F2F4step 3.1∎

By [F1], Qz is invariant under every translation, and step 3.1 with m=1 gives EQzNC0=z<∞. Thus Qz∈Pstat1(Ω) by [F4], which verifies the domain hypothesis of the specific-entropy theorem in [F2]. Its integer-cube supremum formula applied to step 3.1 yields I(Qz)=1−z+zlog⁡z and identifies this value with the real-side cube limit. At z=1 the value is zero, the reference-law baseline.

ExampleConstruction: AI-generatedVerification: AI-generatedverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)Open item page →

A Superlinear Count Test Is Not Local-Tame

Example

For the unit cell C0=[0,1)d, the observable f(ω)=NC0(ω)2 is local but is not one of the tests defining the local-tame topology.

Facts & Assumptions

Given: The unit-cell configuration space and the definition of local-tame observables.

[F1]

Every cell configuration is finite, and NC0 is measurable with respect to the restriction to C0 (Poisson Configuration Reference).

[F2]

A local-tame observable supported in C0 must satisfy ∣f(ω)∣≤b(1+NC0(ω)) for one finite constant b (Local-Tame Topology).

Verification

Given: f(ω)=NC0(ω)2.

1.1F1algebra

By [F1], NC0 is finite and measurable from the restriction to C0, so its square is a measurable local observable.

2.1givenF1F2constructalgebra∎

Fix any finite b≥0 and choose an integer n>b+1. The cell configuration space contains configurations with exactly n distinct points in C0. On each such configuration, f=n2>b(1+n), so the tame growth bound in [F2] fails for this b. Since b was arbitrary, f is not tame.