Dimension-free stability of binary-information contraction

Under the sharp contraction and joining results specified in the paper, we prove linear stability of binary-information contraction on every compact amplitude and correlation rectangle, uniformly over finite cube dimensions. For information psi(a) tending to zero, the constant grows at most as a to the power -4, and this exponent is necessary. The statements apply to arbitrary [-1,1]-valued kernels, without a mean restriction or a pointwise interior margin. We also strengthen the entropy-production inequality D(u) >= F(I(u)) by an absolute positive multiple of (E u) squared times I(u) squared. The proof combines strict Gaussian contraction, a compactness argument for joining laws, and estimates retaining the entropy of rare endpoint values.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-13
DOI
https://doi.org/10.5281/zenodo.23350308
Primary Topic
Probability and Statistical Research
Type
preprint
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preprint

Dimension-free stability of binary-information contraction

Myeongjin Kang
Zenodo (CERN European Organization for Nuclear Research)
Probability and Statistical Research
preprint

Dimension-free stability of binary-information contraction

Myeongjin Kang
preprint en

Abstract

Under the sharp contraction and joining results specified in the paper, we prove linear stability of binary-information contraction on every compact amplitude and correlation rectangle, uniformly over finite cube dimensions. For information psi(a) tending to zero, the constant grows at most as a to the power -4, and this exponent is necessary. The statements apply to arbitrary [-1,1]-valued kernels, without a mean restriction or a pointwise interior margin. We also strengthen the entropy-production inequality D(u) >= F(I(u)) by an absolute positive multiple of (E u) squared times I(u) squared. The proof combines strict Gaussian contraction, a compactness argument for joining laws, and estimates retaining the entropy of rare endpoint values.

Zenodo (CERN European Organization for Nuclear Research)
Probability and Statistical Research
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