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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.

Checkpointed reverse sweep recomputes an intermediate

Example

For x∈R, take the two-transition real-arithmetic computation x1=F1(x)=x2, x2=F2(x1)=sin⁡(x1), with scalar output f(x)=x2. Both transitions are C1 on R. Run the uniform-block checkpoint schedule with T=2 and one saved-state slot m=1. The schedule stores the initial state, recomputes x1 when reversing F2, and returns the same derivative as an uncheckpointed reverse pass: f′(x)=2xcos⁡(x2).

Facts & Assumptions

Given: The two C1 transitions, initial state x, and uniform-block schedule with T=2 and m=1.

[F1]

Uniform-block and binomial checkpoint schedules for reverse sweeps: uniform-block mode saves its scheduled checkpoints, restores a block start, replays the transitions needed to reach each step, records that step's local tape, and reverses the step.

[F2]

Uniform-block and binomial checkpoint schedules preserve reverse adjoints with stated costs: when deterministic replays stay in the declared domains, uniform-block checkpointing returns the same terminal value and initial-state cotangent as the uncheckpointed reverse sweep.

[F3]

The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a): the derivative of a composition of totally differentiable maps is the ordered composition of their total derivatives.

[F4]

Chain sum product and composition rules for Banach derivatives: on the real Banach space R, multiplication (a,b)↦ab is bounded bilinear because ∣ab∣≤∣a∣∣b∣, and the identity map has Fréchet derivative 1. Applying the bounded-bilinear product rule to two identity maps gives D(x↦x2)=2x, a continuous derivative.

[F5]

The derivatives of sine and cosine are cosine and minus sine: on R, D(sin⁡u)=cos⁡u and D(cos⁡u)=−sin⁡u for every u∈R; these one-dimensional derivatives are total derivatives.

[F6]

Total differentiability gives a local O(∥h∥2) increment bound and therefore continuity: a totally differentiable map on R is continuous; in particular, cos⁡ is continuous because it is differentiable by [F5], so u↦sin⁡u is C1.

Verification

1.1F1givenconstructalgebra

Here q=⌈T/m⌉=2, so the uniform-block schedule stores only x0=x and has one block [0,2]. In the reverse pass it first restores x0 and replays F1, obtaining x1=x2 before recording the local tape for F2; it then restores x0 again to process F1. Both transitions are defined on all of R, so every replay stays in its declared domain.

2.1F1F4F5step 1.1algebra

Starting with output cotangent xˉ2=1, the local derivative of F2 gives xˉ1=cos⁡(x2) by [F5], and the local derivative of F1 gives xˉ0=2xcos⁡(x2) by [F4]. This includes x=0, where the derivative is 0, and x=1, where it is 2cos⁡(1).

3.1F2F3step 2.1algebraF6∎

The deterministic replay preserves the local inputs and reverse rules, so [F2] identifies the returned cotangent with the uncheckpointed derivative; [F3] gives f′(x)=F2′(F1(x))F1′(x)=cos⁡(x2) 2x, the same value. The example shows a recomputation trade-off and asserts no schedule optimality.

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