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
- Algebraic Extensions, Extension Degree, and Finite Fields
- Arithmetization and the Sum-Check Protocol
- Binary Operations, Monoids, Groups and Subgroups
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Formal Languages, Encodings, and Decision Problems
- Foundations of the Real Numbers for Analysis
- Interactive Proof Systems and Public Coins
- Linear Recurrences and Rational Generating Functions
- Polynomial Rings, the Division Algorithm and Roots
- Relations, Functions, and Quotients
- Resource Bounds and Machine Invariance
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- The ZFC Axioms and the Basic Set Constructions
- Turing Machines, Configurations, and Computation
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
Interpolating a two-bit table over F_5
Example
Over , let , , , and . Its unique multilinear extension is . In particular in .
Facts & Assumptions
Given: The objects and hypotheses in the statement above.
Every field-valued Boolean table has the unique multilinear extension given by the product-basis interpolation sum (Boolean-cube interpolation).
Verification
The interpolation formula gives . Expanding and combining coefficients yields , which has degree at most one in each variable.
Its values at are respectively , so it agrees with the table; uniqueness follows from the interpolation theorem. At its integer expression is , which reduces to one modulo five.
A complete sum-check transcript over F_101
Example
Over let , with individual bounds and claimed sum . An honest transcript with successive challenges has messages The successive scalar claims are , and the terminal equality holds. For this polynomial, a false initial sum claim is accepted with probability at most 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.
Honest messages are partial cube sums, with coefficient, endpoint-consistency and terminal checks (Sum-check with explicit degree bounds).
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
Sum over Boolean to obtain . Then and . This also computes the cube sum by splitting over .
After , sum over : . Its endpoint sum is , and its value at is .
With that prefix . Its endpoint sum is . At its value is , also . The coefficient lists have lengths and the degrees meet , so all checks pass modulo 101.
For any false initial claim the soundness theorem applies to the fixed polynomial and trusted degree bounds, giving error at most over fresh independent challenges. It asserts this for all adaptive provers, separately from the honest transcript just computed.
Correcting one corrupted table entry
Example
On , let , and let agree with except that . Thus . At every fixed nonzero , the two-query corrector succeeds with probability exactly ; it fails exactly for seeds and . At it always returns zero.
Facts & Assumptions
Given: The objects and hypotheses in the statement above.
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
There are eight cube points, with precisely the zero point corrupted, so . The self-correction theorem gives a unique nearby linear function and success at least at each point.
For fixed , the query is corrupted exactly when , and the query is corrupted exactly when . 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 , the sum is zero for every seed, equal to . Thus the lower bound is attained for nonzero requests and exceeded for the zero request.
One point evaluation for an exponentially large cube
Statement refuted
The assertion that a sum-check verifier must evaluate separately at every Boolean vertex is false. For , take over a supplied finite field, with individual bounds . Its cube sum is one although the cube has points. The verifier uses field work, including one evaluation of with at most multiplications. False initial claims have acceptance probability at most .
Facts & Assumptions
Given: The objects and hypotheses in the statement above.
The verifier checks coefficient messages round by round and evaluates g at the terminal challenge point (Sum-check with explicit degree bounds).
Verifier field work is O(D) plus a single terminal point evaluation with D=sum_i(d_i+1) (Sum-check verifier resources).
The false-claim acceptance probability is at most min(1,sum_i d_i/|F|) (Adaptive-prover soundness of sum-check).
Counterexample
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 vertices, also when .
The specified protocol uses one terminal evaluation. Implement it by multiplying the supplied coordinates starting from one, at a cost of at most multiplications. Here , so the resource lemma bounds all verifier field work, including this evaluation, by . Zeros among the challenge coordinates simply make the product zero, without any division.
The soundness theorem gives the stated false-claim bound from , including the cap at one when the field is small. Thus the verifier does not need separate evaluations of . The claim concerns verifier work with a supplied evaluator and imposes no general efficiency assertion on the prover.
Sources
- Justin Thaler, Proofs, Arguments, and Zero-Knowledge (2023), Figure 3.2 p.31
- Justin Thaler, Proofs, Arguments, and Zero-Knowledge (2023), §4.1 Example Execution pp.36–37
- Arora and Barak, Computational Complexity, January 2007 web draft, §18.4.1 Local decoding pp.364–365
- Justin Thaler, Proofs, Arguments, and Zero-Knowledge (2023), §4.1 protocol and resource analysis pp.33–38