Alphabeta Math
CounterexampleConstruction: 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 systematic composition of reversible kernels need not be reversible

Statement refuted

The chronological composition of two probability kernels that are each reversible with respect to the same invariant probability must itself be reversible with respect to that probability.

Facts & Assumptions

Given: X={0,1,2} with its full sigma-algebra, π(i)=1/3, the transposition σ1 swapping 0 and 1, and the transposition σ2 swapping 1 and 2; each transposition fixes the remaining state.

[A1]

X, π, σ1, and σ2 are as specified above.

[F1]

Reversibility means that the joint flow measure is symmetric under coordinate swap. (General-state reversibility by symmetric flow measure)

[F2]

Reversibility of a probability kernel with respect to a probability implies invariance of that probability. (General-state reversibility implies invariance)

[F3]

Kernel composition is in chronological order: K1K2 first applies K1 and then K2. (Composition of probability kernels)

[F4]

The composition of two probability kernels is again a probability kernel. (Kernel composition is well defined and associative)

[F5]

Invariance means πK(A)=∫K(x,A) π(dx)=π(A) for each measurable A. (Invariant and stationary distribution for a Markov kernel)

[F6]

A probability measure has total mass one. (Probability measures and probability spaces)

[F7]

A probability kernel has probability-measure rows and measurable event evaluations. (Measure kernel and probability kernel)

Counterexample

Given: The data in [A1]. Define Kj(i,A)=1A(σj(i)) for j=1,2 and i∈X.

Proof technique: Check the symmetric flow of each involution, compute the chronological composition, and compare one off-diagonal flow atom with its transpose.

1.1A1F1F6F7given

The uniform values sum to one, so [F6] makes π a probability; for every set A, Kj(i,A)=1 exactly when σj(i)∈A and is otherwise zero, so each row is a probability measure and its evaluations are measurable on the finite space by [F7]. The flow has mass 1/3 at (i,σj(i)); since σj2 is the identity, its support and masses are symmetric, so both kernels are reversible by [F1].

1.2A1F3F4F5given

Chronological composition [F3] gives Q=K1K2 with updates 0↦2, 1↦0, and 2↦1; [F4] makes it a probability kernel. Each state has exactly one predecessor, so πQ({j})=1/3=π(j) for every j and [F5] gives πQ=π.

2.1F2step 1.1

By [F2] and step 1.1, each component kernel preserves the same uniform probability π.

3.1A1F1step 1.1step 1.2step 2.1givenalgebra∎

The composite flow has Λπ,Q({0}×{2})=1/3 but Λπ,Q({2}×{0})=0, so it is not symmetric by [F1]. Thus the composition is invariant but not reversible, although both updates are reversible and invariant by steps 1.1 and 2.1.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

17 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.