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The asymptotic Gotsman-Linial conjecture on average sensitivity of polynomial threshold functions

Theorem 1.1: for n≥1n\ge1, 1≤d≤n1\le d\le n and a real multilinear polynomial pp of degree at most dd, f=sgn(p)f=\mathrm{sgn}(p) with sgn(0)=1\mathrm{sgn}(0)=1 has I(f)≤8dnI(f)\le8d\sqrt n; the constant is absolute and dd may grow with nn. Symmetric examples show the order dnd\sqrt n is sharp for d≤nd\le\sqrt n. It removes the polylogarithmic loss in Kane's bound. It makes no claim about the exact extremizers of the original Gotsman-Linial proposal (already refuted by Chapman and by Kim-Maldonado-Wellens) and does not address non-uniform distributions.

Exact FrontierDelta

Prior state unknown→proved

Scope and record

Occurred: Sep 25, 2026

Delta type: SOURCE CLAIM

Assumptions: VibeMathed verification: lean-checked. Publication: announcement. AI contribution: ai-discovered. VibeMathed editorial classifications, scores, notes, relations, and dataset structure are CC BY 4.0. Source statements and linked content retain their own rights.

Canonical aliases: The asymptotic Gotsman-Linial conjecture on average sensitivity of polynomial threshold functions · Gotsman-Linial conjecture (asymptotic)

Confidence: Not scored

Registry verification: lean checked · announcement · candidate

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Attribution

VibeMathed
registry · event recorded by

Unreleased internal OpenAI model
model · ai model contributor · OpenAI

Artifacts and verifiers

Lean proof (OAI.LeanBlast.GotsmanLinial.gotsmanLinialStatement)

lean artifact · pending

Artifact ↗
Comparator statement: GotsmanLinial.lean

lean artifact · pending

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Compute record

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Lineage and corrections

Lean proof (OAI.LeanBlast.GotsmanLinial.gotsmanLinialStatement) evidence for this event

The asymptotic Gotsman-Linial conjecture on average sensitivity of polynomial threshold functions evidence for this event

VibeMathed record: The asymptotic Gotsman-Linial conjecture on average sensitivity of polynomial threshold functions evidence for this event

This event attributed to Unreleased internal OpenAI model

The asymptotic Gotsman-Linial conjecture on average sensitivity of polynomial threshold functions parent of this event

Theorem 1.1: for n≥1n\ge1, 1≤d≤n1\le d\le n and a real multilinear polynomial pp of degree at most dd, f=sgn(p)f=\mathrm{sgn}(p) with sgn(0)=1\mathrm{sgn}(0)=1 has I(f)≤8dnI(f)\le8d\sqrt n; the constant is absolute and dd may grow with nn. Symmetric examples show the order dnd\sqrt n is sharp for d≤nd\le\sqrt n. It removes the polylogarithmic loss in Kane's bound. It makes no claim about the exact extremizers of the original Gotsman-Linial proposal (already refuted by Chapman and by Kim-Maldonado-Wellens) and does not address non-uniform distributions. parent of this event

Comparator statement: GotsmanLinial.lean evidence for this event

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