Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passverified 2026-09-08 (gpt-6-astra)
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.

Pairwise-independent hashing controls fibre size

Statement

Let U be a finite set with ∣U∣≥2, let R be a nonempty finite set, and let H be a nonempty finite family of maps from U to R. Assume pairwise independence: under a uniform choice of h∈H, for distinct z,z′∈U and all y,y′∈R, Pr⁡(h(z)=y, h(z′)=y′)=∣R∣−2. For S⊆U and y∈R, put N=∣{z∈S:h(z)=y}∣. Then EN=∣S∣/∣R∣ and Var⁡N≤EN. Write μ=∣S∣/∣R∣. The test N>0 has probability at most 1/4 when μ≤1/4, and at least 3/4 when μ≥4.

The corresponding public challenge announces h (and the fixed target y), asks the prover for z, and accepts exactly when z∈S and h(z)=y. Its optimal acceptance probability is Pr⁡(N>0), giving the stated gap between these two promised size regimes. When elements and hashes have polynomial-length encodings, the uniform hash is sampled using polynomially many fair bits, and hash evaluation and membership in S are polynomial-time decidable, this is a polynomial-time public-coin challenge: the verifier announces every sampling bit before receiving the response, then performs the stated deterministic checks.

Proof

Given: S⊆U with ∣U∣≥2, a pairwise-independent family, and y∈R.

[F1] Expectation is the finite weighted sum in Expectation of a real random variable on a finite probability space, here with each hash of weight 1/∣H∣; variance is the squared-deviation expectation in Variance, standard deviation, and covariance on a finite probability space.

1.1givenF1algebra

Write N=∑z∈SIz, where Iz indicates h(z)=y. Summing the assumed joint probabilities over outputs at any distinct input gives EIz=1/∣R∣. For distinct z,z′, E(IzIz′)=∣R∣−2, so their covariance is zero by expansion of its defining finite sum.

2.1F1step 1.1algebra

Interchanging finite sums gives EN=∣S∣/∣R∣. Expanding (N−EN)2 and using the zero cross terms gives Var⁡N=∑zVar⁡Iz. Since Iz2=Iz, each summand is ∣R∣−1(1−∣R∣−1). Thus Var⁡N=μ(1−1/∣R∣)≤μ.

3.1step 2.1algebra

Since N is a nonnegative integer, 1{N>0}≤N, so Pr⁡(N>0)≤μ≤1/4 in the small regime. For μ>0, on N=0 one has (N−μ)2=μ2, hence μ2Pr⁡(N=0)≤E(N−μ)2≤μ. Therefore Pr⁡(N>0)≥1−1/μ≥3/4 when μ≥4.

4.1givenstep 3.1∎

If N=0, no prover response passes the stated checks. If N>0, an unrestricted prover can supply a valid preimage. Thus the optimal acceptance probability is exactly Pr⁡(N>0). The checks are polynomial-time under the stated encoding and algorithmic hypotheses, and revealing every sampling bit before the response makes the protocol public-coin. This proves the quantitative challenge claim.

Depends on

Used by

Dependency tree · two levels

6 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