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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.

✓ 3 results · all verified · 2 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 1 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Decidable, Recognizable, and Enumerable Languages: Examples and Counterexamples

1 · Prerequisites

2 · Summary

These examples keep the classification page concrete. One pair of trivial recognizers dovetails to a genuine decider, a few short bit strings illustrate the machine-code validity test, and the final counterexample isolates the gap between recognizability and total halting on nonmembers.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-12Open item page →

Recognizers for strings containing a 1 and for all-zero strings

Example

Let A⊆{0,1}∗ be the language of binary strings containing at least one 1. Its complement consists of the all-zero strings, including the empty word.

Facts & Assumptions

Given: The language A and its complement Ac.

[L1]

By A language is decidable if and only if it and its complement are recognizable, recognizers for a language and its complement can be interleaved into a decider.

Verification

technique · direct
1.1given

A recognizer for A scans the input from left to right and accepts as soon as it sees a 1; on an all-zero input it may simply continue forever. A recognizer for Ac scans to the end of the input and accepts exactly when every scanned symbol is 0.

2.1L1step 1.1∎

On any input word, exactly one of the two recognizers from step 1.1 eventually accepts. Therefore [L1] interleaves them into a decider for A.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-12Open item page →

Testing a few short binary strings for machine-code well-formedness

Example

Under the chosen code of A fixed effective binary encoding of deterministic one-tape Turing machines, compare the short words 0,10,111111011011100101101100101.

Facts & Assumptions

Given: The three displayed binary words.

[L1]

By The chosen machine coding is injective and has a total decoder, the decoder either reconstructs one coded machine or reports malformed input.

[L2]

By Well-formed deterministic one-tape Turing machine descriptions form a decidable language, this decoder test decides well-formed machine descriptions.

Verification

technique · direct
1.1L1given

The words 0 and 10 are malformed. The first never even begins the required positive-arity tuple code, and the second announces arity 1 but provides no self-delimiting payload block. Therefore the decoder of [L1] rejects both.

1.2L1L2given

The word 111111011011100101101100101 is the code of the tuple (11,0,1,1,0,1), so it decodes to the normalized machine with m=3, s=0, n=1, and the unique transition (0,0)↦(1,0,1). In particular 0≤s<n, and the tuple has the required 3+3(m−2)n=6 entries. Thus [L1] accepts it as a well-formed machine description, and [L2] classifies the three sample words exactly as claimed.

2.1step 1.1step 1.2∎

This makes the decidability test concrete: two malformed inputs are rejected and one genuine machine code is accepted.

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passaudited 2026-09-12Open item page →

A recognizer for {0} that loops on every nonmember

Statement refuted

Whenever a machine recognizes a language, it halts on every input.

Facts & Assumptions

Given: The one-word language {0}⊆{0,1}∗.

[L1]

By Decidable and recognizable languages, recognizers are required to accept the members of their language, but they may diverge on nonmembers.

[L2]

By FALSE: recognizable means total halting, the statement claims that such divergence cannot occur.

Counterexample

technique · direct
1.1L1given

Let M accept immediately on input 0 and otherwise move right forever without entering any halting state. Then [L1] shows that M recognizes exactly {0}.

2.1L2step 1.1

Every nonmember of {0} makes M diverge, so M is not total. This contradicts the claim summarized in [L2].

3.1step 1.1step 2.1∎

Therefore M is a concrete counterexample to the refuted statement.

Sources