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.
A random evaluation test that separates the zero polynomial from x^2-y^2
Example
Take the polynomial over , and sample uniformly from the grid with . Then vanishes on exactly three of the nine grid points, so one random evaluation misses its nonzeroness with probability .
Facts & Assumptions
Given: the polynomial and the sample set .
PIT tests whether a represented polynomial is identically zero by random evaluation in an admissible representation (Polynomial identity testing).
The Schwartz-Zippel lemma bounds the vanishing probability of a nonzero polynomial on a finite grid (The Schwartz-Zippel lemma).
Under the usual degree and evaluation assumptions, PIT lies in coRP (Polynomial identity testing is in coRP).
Verification
The equality on is equivalent to because all elements of are nonnegative rationals and squaring is injective there. Hence the roots on the grid are exactly , , and .
So a random point of makes vanish with probability . This is consistent with [L2], since the total degree is and . Using the random-evaluation viewpoint from [L1], this is exactly the one-sided coRP behavior asserted in [L3], because the zero polynomial would vanish on every sample point.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
10 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
- Sanjeev Arora and Boaz Barak, Computational Complexity: A Modern Approach (standard reference, not scraped)