Is block sensitivity at most quadratic in sensitivity? (Nisan-Szegedy)
For a total Boolean function , the sensitivity is the largest number of single bits whose flip changes at some input, and the block sensitivity the largest number of disjoint blocks of bits each of whose flip changes at some input. Nisan and Szegedy (1994) explicitly suggested . Rubinstein's functions give , improved to by Ambainis and Sun (2011); Huang's 2019 proof of the Sensitivity Conjecture gives . Is there a universal constant with for every total Boolean function?
- Result
- Disproved(see note)
- Status
- Candidate (review pending)
- AI contribution
- AI-discovered
- Method
- Construction
- Field
- Boolean function complexity; sensitivity
- Posed by
- Noam Nisan and Mario Szegedy (Section 4, Computational Complexity 1994)
- Year posed
- 1994
- Years open
- 32y
- Solved
- 2026-09-25
- Model
- Unreleased internal OpenAI model
- Vendor
- OpenAI
- Collaborators
- —
- Verification
- Lean-checked, statement unaudited
- Publication
- Announced
- Collection
- OpenAI math release (October 2026), version adc7f12
- Significance
- 35 / 100
- Disclosed cost
- —
- Wikipedia
- No dedicated article
What was actually shown
Theorem 1.1: for every integer an explicit recursive construction gives a nonconstant total Boolean function with , so no bound holds; the abstract also claims a fixed with along an unbounded family. This does not close the gap to Huang's upper bound ; the true exponent remains open.
What the AI did
The release README says every result in openai/math was produced by an unreleased internal OpenAI model with a fixed procedure, on average about three hours of ChatGPT Pro thinking compute per result. This result is not one of the README's two exceptions (the Hodge conjecture for CM abelian varieties and the Re(s) > 11/12 zero-free region). The manuscript is authored 'OpenAI' and names no human author. The manuscript adapts the gated-tournament architecture of Meiburg's 2026 spectral-sensitivity separation (arXiv 2608.00851), whose own attribution credits GPT-5.6-Sol with those ideas.
Verification
No independent mathematician has checked this yet. Checked here: Theorem 1.1 was read against the quadratic question: for every some nonconstant total has , with for each . lean/formalization.yaml lists SensitivitySeparation.json (declaration OAI.Paper320.quantitative_separation, file OAI/Combinatorics/Sensitivity/Separation.lean). The statement SensitivitySeparation.lean was read here; not rebuilt here. It defines sensitivity and block sensitivity (disjoint nonempty blocks) as in the paper and states, for every , a nonconstant on Fin n inputs with ; the ratio is unbounded, so this states the headline disproof. The abstract's fixed exponent is not in this statement. Permitted axioms: propext, Quot.sound, Classical.choice.