Alphabeta Math
ExampleConstruction: Literature-sourcedVerification: AI-adaptedPipeline-generatedjudge 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 polynomial activation confines univariate outputs to bounded degree

Example

Assume Countable Choice. Let q∈N0 and use the polynomial activation σ(t)=tq (with t0=1). Every finite single-hidden-layer output restricted to [0,1] is a polynomial of degree at most q, and the target f(t)=tq+1 stays at uniform distance at least (q+1)!/[2q+2(q+1)q+1] from every such output.

Facts & Assumptions

Given: q∈N0, σ(t)=tq, and the target tq+1 on [0,1].

[F1]

Polynomial activation functions satisfy the hypotheses of the bounded-degree obstruction with their exact polynomial representative (Polynomial activations have a bounded-degree obstruction).

[F2]

A single-hidden-layer output is a finite affine combination of activation units (Single-hidden-layer network realization).

[A1]

Countable Choice is the assumption of the cited obstruction lemma (The Axiom of Countable Choice (ACω)).

Verification

1.1givenF2algebra

By [F2], each unit on a univariate input is (wt+b)q, a polynomial of degree at most q; finite weighted sums and an output bias preserve that degree bound.

2.1givenF1A1∎

Apply [F1] with the polynomial p(t)=tq to obtain the stated positive lower bound for approximation of tq+1.

Depends on

Used by

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Dependency tree · two levels

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Sources