arXiv:2605.26188v1
Abstract
We give a simple proof of a recent result by J. Schleischitz dealing with a counterexample to the uniform Littlewood conjecture. Our construction is based on simple properties of Fibonacci numbers.
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Detailed mathematical audit
01Statements2 reported findingsCorrect
The Fibonacci construction gives real numbers whose uniform Littlewood product has limsup at least , as stated.
The uniform Littlewood lower bound follows from the Fibonacci residue construction
Pages 2–3 · Theorem 1 and Sections 4–5 · arXiv:2605.26188v1
For each selected Fibonacci denominator, the coprime residue is chosen so that both normalized coordinates fall in prescribed shrinking intervals. The continued-fraction separation estimate then gives a uniform lower bound for every , and the approximation errors vanish uniformly as .
Full paper, version 1 ↗The finite Fibonacci product bound approaches the stated constant
Pages 2–3 · formulas (2)–(4) · arXiv:2605.26188v1
If is not a convergent to , the standard separation applies. For a convergent , the exact continued-fraction error has denominator approaching . Thus, for every fixed and all sufficiently large , the product is at least throughout ; letting gives the theorem's constant.
02Proofs2 reported findingsCorrect
The uniform-distribution lemma, Fibonacci separation estimate, and limiting argument are correct and complete.
Coprime residues can be localized in both prescribed intervals
Pages 1 and 4 · Lemma 1 and Section 6 · arXiv:2605.26188v1
Uniform distribution first gives an integer whose two normalized coordinates lie strictly inside the target intervals. A nearby Fibonacci number is coprime to , and the cited short-shift lemma supplies for which is coprime to . Since , this shift changes both normalized coordinates by and keeps them inside the target intervals.
The finite-level bounds pass uniformly to the constructed limit
Pages 1–4 · auxiliary lemmas and proof of Theorem 1 · arXiv:2605.26188v1
The uniform-distribution and short coprime-shift argument gives a residue in the two prescribed intervals, and the convergent/non-convergent dichotomy supplies the sharp finite Fibonacci estimate. The errors are uniform over the entire minimization range, so taking the limsup is justified.
03Novelty0 reported findingsNo non-novelty findings
No non-novelty findings.