The sharp level-one Fourier weight at density one eighth
Selected result in this collection
Problem and result
- Problem
- Lei Yu's problem on the level-1 Fourier weight W(1/8) of Boolean functions of density 1/8
- Posed in
- Lei Yu, 'On Average Distance, Level-1 Fourier Weight, and Chang's Lemma', arXiv:2504.02593 (v2, 2026 revision), Section VII, Problem 2
- Status
- Determined exactly
- Result
- Every Boolean function of mean 1/8 on {−1,1}^n has level-one Fourier weight at most 3/64, attained by a codimension-three subcube for n ≥ 3; equivalently, density-1/8 sets have average Hamming distance ≥ (n − 3)/2.
Abstract
For every Boolean indicator of mean 1/8 on a finite uniform cube, the level-one Fourier weight is at most 3/64. A codimension-three subcube attains the bound in every dimension at least three.
Details
- Year
- 2026
- Discipline
- Mathematics
- Area
- Discrete analysis
- Subject
- Discrete analysis, tilings, probability and entropy
- Result type
- Sharp finite-cube Fourier bound
- Formal proof
- Standalone Lean package
- Family
sharp-eighth- Keywords
- Fourier analysis of Boolean functions, level-one Fourier weight, level-one inequality, Boolean cube, subcubes, sharp inequality
Statement and scope
Formal scope. The formal proof includes arbitrary dimensions, the exact moment identities, the Hamming-distance bound, and sharpness witnesses.
A Lean check establishes only the formal statement that is checked. It does not establish novelty, historical priority or a faithful transcription of an external source. Known gaps are listed in UNFINISHED.md; the authorship disclosure describes how the results were produced.
Manuscript
- PDF on this site
manuscript.pdf329 KB- PDF on GitHub
papers/mathematics/ sharp-eighth/ manuscript.pdf - LaTeX source
papers/mathematics/ sharp-eighth/ manuscript.tex
Formal proof
This result has a standalone Lean 4 package, separate from the shared library. Build it with the toolchain and instructions given in its README.
- Proof package
papers/3 Lean filesmathematics/ sharp-eighth/ - Lakefile
papers/mathematics/ sharp-eighth/ lakefile.lean - Toolchain
leanprover/lean4:v4.19.0 - Library roots
Reproduce
The commands below are quoted from papers/, which gives their context and working directory.
Reproduce the proof
lake exe cache get
lake build
lake env lean -DwarningAsError=true Audit.lean- README
papers/mathematics/ sharp-eighth/ README.md - Repository guide
verification/README.md - Unfinished proofs
UNFINISHED.md
Source and prior work
Prior work. Yu, On Average Distance, Level-1 Fourier Weight, and Chang’s Lemma, 2026 revision.
Priority and subsequent correspondence. On 30 October 2026, Professor Lei Yu informed me that Yijia Fang and Hao Huang had already proved this bound, with the equality cases, in a manuscript shared privately about a month earlier and submitted to arXiv on 29 October 2026 (arXiv:2610.40486). I make no originality or priority claim for the bound. Priority and novelty record.
Questions and answers
What is the maximal level-one Fourier weight at density 1/8?
It is 3/64 in every dimension n ≥ 3: every Boolean indicator of mean 1/8 has level-one Fourier weight at most 3/64, and a codimension-three subcube attains it. Equivalently, a subset of density 1/8 of the Boolean cube has average pairwise Hamming distance at least (n − 3)/2.
Was this bound proved before?
Yes. Yijia Fang and Hao Huang proved it earlier, together with a characterization of the equality cases (arXiv:2610.40486, submitted 29 October 2026; reported to have circulated privately about a month before). This note gives an independent certificate-based proof, does not classify equality cases and makes no originality or priority claim.
Is the proof formally verified?
The Lean 4 formalization covers arbitrary dimensions, the exact moment identities, the Fourier and Hamming-distance bounds and the sharpness witnesses, and a separate Python checker verifies the rational certificate in exact arithmetic. The note is an unrefereed preprint.
Cite
Citation file papers/. For the whole collection, use CITATION.cff or CITATION.bib.
@misc{goyal2026sharpeighth,
author = {Goyal, Dev D.},
title = {The sharp level-one Fourier weight at density one eighth},
year = {2026},
howpublished = {Preprint},
url = {https://github.com/dvgyl/math-and-theory/tree/main/papers/mathematics/sharp-eighth},
note = {Fully autonomous agentic research generated by AI systems.}
}