Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-generatedPipeline-generatedjudge 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.

Repetition-code syndrome, known erasures, and unlocated errors

Example

Use the binary repetition code C={000,111} and H=[110011]. A single middle-bit error in 111 gives r=101 and a syndrome identifying that error. Two known erasures at coordinates 0,1 still leave a unique completion when coordinate 2 is received. By contrast, two unlocated errors in 000 can produce r=110, which lies closer to the wrong codeword 111.

Facts & Assumptions

Given: The binary repetition code C={000,111}, the parity-check matrix H above, a received word r=101, known erasures S={0,1} in zero-based coordinates, and the alternative unlocated-error word r′=110.

[F2]

Rectangular matrix multiplication and the identity matrix In, including zero-sized shapes: Matrix multiplication uses finite coordinate sums over the field.

[L1]

Generator and parity-check matrices over a finite field: A parity-check matrix has independent rows and nullspace equal to C.

[L2]

Hamming weight and distance of a finite-field linear code: Weight counts nonzero coordinates, and d(C) is the minimum nonzero codeword weight.

[L3]

Syndrome of a received word over a finite field: The syndrome of a received word r is s(r)=Hr.

[L4]

Classical distance gives error detection, unique decoding, and erasure recovery: Distance 3 uniquely decodes one unknown error and gives unique erasure completion on every known support of size less than 3.

Verification

1.1F1F2L1L2givenalgebra

The codewords are 000 and 111, and [L2] gives d(C)=3. For x=(x0,x1,x2), multiplication gives Hx=(x0+x1,x1+x2), so Hx=0 exactly when x0=x1=x2 and ker⁡H=C. The first two columns of H form [1101], an invertible triangular matrix over F2, so the rows of H are independent. Therefore H is a parity-check matrix by [L1]; the field operations are valid by [F1].

1.2L1L3L4givenalgebra

Direct multiplication gives the weight-at-most-one syndromes s(000)=00, s(100)=10, s(010)=11, and s(001)=01, all distinct. For r=101, [L3] gives s(r)=Hr=(1+0,0+1)=11, so the unique matching error is e=010 and r−e=111. The distinct syndromes give the one-error correction in [L4].

1.3L4givenalgebra

If coordinates 0,1 are erased in r=101, the received value at coordinate 2 is 1, so 111 is compatible and 000 is not. More generally, two compatible codewords would differ only in S, so their distance would be at most 2<3, contradicting [L4]. Hence 111 is the unique completion.

2.1L4givenalgebra∎

If instead 000 is sent, the two-bit error e=110 produces r′=110. Then dH(r′,000)=2 but dH(r′,111)=1, so 111 is the unique radius-one decoding candidate. The unique-radius-one rule therefore miscorrects this word; [L4] gives no correction guarantee for the two-bit error.

Remarks

Coordinates are zero-based. The example distinguishes a single unknown error from two known erasures and from two unknown errors; the code is guaranteed to correct one unknown error, while known locations carry stronger information.

Depends on

Used by

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Dependency tree · two levels

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Sources