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.
Finite FK random-cluster weights for real q and boundary partitions
Definition
Let be a finite undirected multigraph: and the edge-label set are finite, and each has a singleton or two-element endpoint set . Distinct labels may have the same endpoint set. Let be a designated boundary set and let be an equivalence relation on ; vertices in each -class are wired together, while vertices outside remain separate. For , let be the number of connected components, including isolated vertices, after wiring the -classes and opening exactly the edges in . A loop does not join distinct vertices.
For and , the finite FK weight of and its partition function are
The finite FK random-cluster probability is . Every weight is positive and finite, and the finite sum is positive and finite. The free boundary condition is the identity partition on ; the wired boundary condition identifies all vertices of in one class.
When is a finite simple graph with the Potts/FK convention of the earlier finite Potts definition, is free, is an integer at least two, and every activity equals , this definition gives the same normalized FK law, since .