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.
Nonunique minimizers allow distinct local accumulation limits
Example
Suppose has two distinct stationary minimizers with equal finite value . Every finite mixture , , also minimizes . The sequence of stationary laws alternating between and has two distinct local accumulation points and does not converge. This conditional mathematical example shows why compactness alone yields minimizer-set accumulation; it does not assert a finite-volume ensemble realizes the sequence or that a particular continuum potential exhibits coexistence.
Facts & Assumptions
Given: Two distinct stationary minimizers with finite equal minimum .
Superstable regular continuum grand pressure and variational formula: is the limit of normalized finite-volume expected energies, and is expected unit-cell count.
Specific Relative Entropy Density: is the large-cube limit of relative entropies against the unit-intensity Poisson reference.
Relative Entropy Variational, Chain, and Conditioning Rules: relative entropy obeys the mixture-label chain rule and is nonnegative.
Local-Tame Topology: tests every bounded measurable local observable.
Verification
Given: The assumptions above.
Let be stationary laws of finite specific entropy and let . For a bounded cube , the binary-label chain rule [F3] gives where is the mixture label and the observed configuration. Since , divide by and take the cube limit [F2]; , so . The expectation defining is affine. For finite energy densities, for every ; dividing by volume and taking limits gives affinity of . Thus is affine on finite mixtures of finite- laws.
Applying step 1.1 to gives , so every mixture is a minimizer. Put and . The even and odd subsequences converge locally to and . Since distinct laws differ on a bounded-region measurable event, its indicator is a bounded local test by [F4] with different expectations; the full sequence therefore has no limit. Both accumulation points are minimizers.