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 three-CNF formula as a fixed-alphabet binary constraint graph
Statement
For a three-CNF formula with clauses, each having exactly three literal occurrences, there is a polynomial-time binary constraint graph over the fixed alphabet with exactly edges. Its value is one exactly when is satisfiable. If is unsatisfiable and , then The zero-clause formula maps to an edgeless graph.
Facts & Assumptions
A CNF clause is a disjunction of literals, and each literal is a variable or its negation. (Boolean formulas, conjunctive normal form, and the satisfiability language SAT)
A binary constraint graph carries a finite nonempty alphabet, explicit binary edge relations, and has value one when it is edgeless. (Constraint graph and labeling value)
Proof
Given: Write each clause as , where each is a literal on an old Boolean variable.
For each old variable add one shared bit vertex. For each clause add a private tuple vertex and put . For each occurrence , add a separate edge from to the old variable in with relation . Thus every clause contributes exactly three edges, including parallel edges when variables repeat, for a total of .
If is satisfied by an assignment , label each old vertex by and each by the tuple of the three variable values at its occurrences. That tuple lies in , so all three edges of every clause star pass and .
Conversely, if all graph edges pass, each clause tuple vertex has a label with , and each edge forces its occurrence coordinate to equal the corresponding old bit label. A repeated variable uses the same old vertex on every occurrence edge, so the equalities are consistent. Therefore each original clause is true under the old bit labels; hence is satisfiable.
The two implications prove exactly when is satisfiable. If , the graph is edgeless, has value one by convention, and the empty conjunction is true. If and is unsatisfiable, step 2.2 shows no graph labeling can satisfy all edges, so every labeling violates at least one and . The alphabet and each edge table have constant size, so the explicit construction is polynomial time.
Depends on
Used by
Dependency tree · two levels
3 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.
Sources
- Irit Dinur, The PCP Theorem by Gap Amplification (standard reference, not scraped)
- Sanjeev Arora and Boaz Barak, Computational Complexity: A Modern Approach (standard reference, not scraped)