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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.

Arithmetization and the Sum-Check Protocol: Examples and Counterexamples

1 · Prerequisites

2 · Summary

These examples expand a two-bit interpolation table, check every round of a three-variable sum-check transcript, and count the successful seeds of a noisy linear self-corrector. The product-polynomial counterexample makes the verifier’s cost explicit even when the Boolean cube has exponentially many points.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Interpolating a two-bit table over F_5

Example

Over F5, let f(0,0)=1, f(0,1)=2, f(1,0)=1, and f(1,1)=4. Its unique multilinear extension is f~(X,Y)=1+Y+2XY. In particular f~(2,3)=1 in F5.

Facts & Assumptions

Given: The objects and hypotheses in the statement above.

[F1]

Every field-valued Boolean table has the unique multilinear extension given by the product-basis interpolation sum (Boolean-cube interpolation).

Verification

1.1

The interpolation formula gives (1X)(1Y)+2(1X)Y+X(1Y)+4XY. Expanding and combining coefficients yields 1+Y+2XY, which has degree at most one in each variable.

F1algebra
2.1

Its values at (0,0),(0,1),(1,0),(1,1) are respectively 1,2,1,4, so it agrees with the table; uniqueness follows from the interpolation theorem. At (2,3) its integer expression is 1+3+223=16, which reduces to one modulo five.

F1step 1.1algebra
ExampleConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

A complete sum-check transcript over F_101

Example

Over F101 let g(X,Y,Z)=2X3+XZ+YZ, with individual bounds (3,1,1) and claimed sum H=12. An honest transcript with successive challenges 2,3,6 has messages h1(X)=8X3+2X+1,h2(Y)=34+Y,h3(Z)=16+5Z. The successive scalar claims are 69,37,46, and the terminal equality holds. For this polynomial, a false initial sum claim is accepted with probability at most 5/101 by the randomized protocol; a displayed fixed challenge sequence alone is not a soundness proof.

Facts & Assumptions

Given: The objects and hypotheses in the statement above.

[F1]

Honest messages are partial cube sums, with coefficient, endpoint-consistency and terminal checks (Sum-check with explicit degree bounds).

[F2]

For any false initial claim, acceptance is at most min(1,sum_i d_i/|F|) under fresh independent challenges and trusted evaluation (Adaptive-prover soundness of sum-check).

Verification

1.1

Sum g over Boolean Y,Z to obtain q1(X)=8X3+2X+1. Then q1(0)+q1(1)=1+11=12 and q1(2)=64+4+1=69. This also computes the cube sum by splitting over X.

F1algebra
2.1

After r1=2, sum g(2,Y,Z) over Z=0,1: q2(Y)=16+(16+2+Y)=34+Y. Its endpoint sum is 34+35=69, and its value at r2=3 is 37.

F1step 1.1algebra
3.1

With that prefix q3(Z)=g(2,3,Z)=16+5Z. Its endpoint sum is 16+21=37. At r3=6 its value is 46, also g(2,3,6)=16+12+18=46. The coefficient lists have lengths 4,2,2 and the degrees meet (3,1,1), so all checks pass modulo 101.

F1step 2.1algebra
4.1

For any false initial claim the soundness theorem applies to the fixed polynomial and trusted degree bounds, giving error at most (3+1+1)/101=5/101 over fresh independent challenges. It asserts this for all adaptive provers, separately from the honest transcript just computed.

F2algebra
ExampleConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Correcting one corrupted table entry

Example

On F23, let (x)=x1, and let f agree with except that f(0)=1. Thus dist(f,)=1/8. At every fixed nonzero x, the two-query corrector succeeds with probability exactly 6/8=3/4; it fails exactly for seeds y=0 and y=x. At x=0 it always returns zero.

Facts & Assumptions

Given: The objects and hypotheses in the statement above.

[F1]

At distance delta<1/4 from a linear function, the two-query corrector succeeds at every point with probability at least 1-2delta, and that nearby linear function is unique (Pointwise correctness under less than quarter noise).

Verification

1.1

There are eight cube points, with precisely the zero point corrupted, so δ=1/8<1/4. The self-correction theorem gives a unique nearby linear function and success at least 12δ=3/4 at each point.

F1algebra
2.1

For fixed x0, the query y is corrupted exactly when y=0, and the query x+y is corrupted exactly when y=x. These are distinct seeds. At either seed exactly one summand is flipped, so the output is wrong; at the other six seeds neither is flipped and linearity gives the right answer. At x=0, the sum f(y)+f(y) is zero for every seed, equal to (0). Thus the lower bound is attained for nonzero requests and exceeded for the zero request.

step 1.1algebra
CounterexampleConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

One point evaluation for an exponentially large cube

Statement refuted

The assertion that a sum-check verifier must evaluate g separately at every Boolean vertex is false. For n1, take g(X1,,Xn)=iXi over a supplied finite field, with individual bounds di=1. Its cube sum is one although the cube has 2n points. The verifier uses O(n) field work, including one evaluation of g with at most n multiplications. False initial claims have acceptance probability at most min(1,n/F).

Facts & Assumptions

Given: The objects and hypotheses in the statement above.

[F1]

The verifier checks coefficient messages round by round and evaluates g at the terminal challenge point (Sum-check with explicit degree bounds).

[F2]

Verifier field work is O(D) plus a single terminal point evaluation with D=sum_i(d_i+1) (Sum-check verifier resources).

[F3]

The false-claim acceptance probability is at most min(1,sum_i d_i/|F|) (Adaptive-prover soundness of sum-check).

Counterexample

1.1

At the all-one vertex the product equals one, and at every other Boolean vertex a zero coordinate makes it zero. Hence the cube sum is one. There are two independent choices per coordinate, giving 2n vertices, also when n=1.

givenalgebra
1.2

The specified protocol uses one terminal evaluation. Implement it by multiplying the n supplied coordinates starting from one, at a cost of at most n multiplications. Here D=i(1+1)=2n, so the resource lemma bounds all verifier field work, including this evaluation, by O(n). Zeros among the challenge coordinates simply make the product zero, without any division.

F1F2algebra
2.1

The soundness theorem gives the stated false-claim bound from idi=n, including the cap at one when the field is small. Thus the verifier does not need 2n separate evaluations of g. The claim concerns verifier work with a supplied evaluator and imposes no general efficiency assertion on the prover.

F3step 1.1step 1.2algebra

Sources