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The Friedgut-Kalai sharp-threshold conjecture: threshold width O(1/log^2 n) for monotone graph properties

Theorem 1.1: for every n≥2n\ge2, nontrivial increasing vertex-symmetric graph property and 0<ε<1/20<\varepsilon<1/2, p1−ε−pε≤219log⁡(1/(2ε))/(log⁡n)2p_{1-\varepsilon}-p_\varepsilon\le2^{19}\log(1/(2\varepsilon))/(\log n)^2. It follows from Theorem 1.2, Varp(f)≤217Ip(f)/(log⁡n)2\mathrm{Var}_p(f)\le2^{17}I_p(f)/(\log n)^2 for every vertex-symmetric Boolean ff and every 0<p<10<p<1, without monotonicity, which removes the (log⁡log⁡n)2(\log\log n)^2 loss of Kelman-Kindler-Lifshitz-Minzer-Safra at p=1/2p=1/2. The order is optimal for fixed ε\varepsilon (clique example). Not shown: the best constant, or where thresholds lie (a different question).

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 Friedgut-Kalai sharp-threshold conjecture: threshold width O(1/log^2 n) for monotone graph properties · Friedgut-Kalai threshold width, graphs

Confidence: Not scored

Registry verification: lean checked · announcement · candidate

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Unreleased internal OpenAI model
model · ai model contributor · OpenAI

Artifacts and verifiers

Lean proof module (OAI.Problem313.sharp_threshold_width)

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Comparator statement: SharpThreshold.lean

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Lineage and corrections

Comparator statement: SharpThreshold.lean evidence for this event

Friedgut-Kalai, Every monotone graph property has a sharp threshold (1996) evidence for this event

The Friedgut-Kalai threshold-width conjecture for r-uniform hypergraph properties evidence for this event

The Friedgut-Kalai sharp-threshold conjecture: threshold width O(1/log^2 n) for monotone graph properties parent of this event

This event attributed to Unreleased internal OpenAI model

Lean proof module (OAI.Problem313.sharp_threshold_width) evidence for this event

Theorem 1.1: for every n≥2n\ge2, nontrivial increasing vertex-symmetric graph property and 0<ε<1/20<\varepsilon<1/2, p1−ε−pε≤219log⁡(1/(2ε))/(log⁡n)2p_{1-\varepsilon}-p_\varepsilon\le2^{19}\log(1/(2\varepsilon))/(\log n)^2. It follows from Theorem 1.2, Varp(f)≤217Ip(f)/(log⁡n)2\mathrm{Var}_p(f)\le2^{17}I_p(f)/(\log n)^2 for every vertex-symmetric Boolean ff and every 0<p<10<p<1, without monotonicity, which removes the (log⁡log⁡n)2(\log\log n)^2 loss of Kelman-Kindler-Lifshitz-Minzer-Safra at p=1/2p=1/2. The order is optimal for fixed ε\varepsilon (clique example). Not shown: the best constant, or where thresholds lie (a different question). parent of this event

VibeMathed record: The Friedgut-Kalai sharp-threshold conjecture: threshold width O(1/log^2 n) for monotone graph properties evidence for this event

Companion: A Sharp Threshold Bound for Monotone Graph Properties evidence for this event

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The Friedgut-Kalai sharp-threshold conjecture: threshold width O(1/log^2 n) for monotone graph properties — Mathematical Frontier Network