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ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
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.

A moving uniform support contributes a missing score boundary term

Example

For θ>0, let Xθ∼Unif⁡(0,θ) and use constant payoff H(x)=1. The objective is J(θ)=1, so J′(θ)=0. The pathwise representation Xθ=θU, U∼Unif⁡(0,1), has derivative estimator 0. The interior score is −1/θ, so E[H(Xθ)Sθ(Xθ)]=−1/θ; it misses the moving endpoint contribution +1/θ.

Facts & Assumptions

Given: θ>0, the density pθ(x)=θ−11(0,θ)(x) with respect to Lebesgue measure, and the constant payoff H(x)=1.

[A1]

The Axiom of Choice: every family of nonempty sets has a choice function; AC is inherited by the score definition.

[F1]

Score and Fisher information: on the positive-density set the score is ∂θlog⁡pθ(x).

[F2]

Pathwise and reparameterized simulation-gradient estimators: in a representation using a fixed random input U, the pathwise estimator is the derivative with U held fixed.

[F3]

Simulation objectives and unbiased payoff oracles: an objective is the expectation of its integrable simulation payoff.

[F4]

Integrating against a density agrees with integrating the product: expectation under density pθ is integration of the payoff times pθ against Lebesgue measure.

[F5]

Expectation of a nonnegative or integrable random variable: the expectation of an integrable constant random variable is that constant.

Verification

1.1givenF2F3F4algebra

The density is normalized because ∫0θθ−1 dx=1. By [F3] and [F4], the constant payoff has J(θ)=∫0θθ−1 dx=1, hence J′(θ)=0. In the representation Xθ=θU, H(Xθ)=1 for every U, so its pathwise derivative is zero by [F2].

2.1A1F1F5step 1.1algebra

On 0<x<θ, pθ(x)=1/θ, so [F1] gives Sθ(x)=−1/θ almost surely under Pθ. Since H=1, the score product is the constant −1/θ almost surely, and [F5] gives Eθ[H(Xθ)Sθ(Xθ)]=−1/θ. This differs from J′(θ)=0, so the fixed-support score identity does not apply.

3.1givenstep 1.1step 2.1algebra∎

The integral is J(θ)=θ⋅θ−1. Differentiating this product gives the interior density-derivative contribution ∫0θH(x)∂θ(θ−1) dx=−1/θ and the moving-endpoint contribution H(θ)pθ(θ−)=1/θ, where pθ(θ−) is the left limit of the density. Their sum is zero, exactly J′(θ) from step 1.1; the interior score term alone omits this boundary correction.

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