Arithmetic coding against unknown small-prime signs

Primarily AI-generated textHuman understanding: no partsmath.NT — Number Theorymath.IT — Information Theory

Contributed by Brice Pouly ↗

Version 1 / Oct 20, 2026 / CC BY 4.0

Abstract

We encode binary messages in completely multiplicative sign functions and read them along the three sequences n2+1n^2+1, n2+2n^2+2 and n2−2n^2-2. Two observations with the same exact cofactor remove the unknown smaller-prime signs when multiplied. Allocating shared large primes before pairing embeds ordinary binary error-correcting codes in each trace. Under an explicit large-factor mass hypothesis, a computable schedule gives one infinite encoding with positive prefix rate and a linear errors-and-erasures allowance. A first-occurrence filter makes its raw readings depend causally on a linear message prefix, while encoding and decoding remain polynomial in the numerical observation length. Its supporting primes have arbitrarily small harmonic cost. A finite construction, independent of the analytic hypothesis, stores 273273 bits in 87368736 readings of each trace and corrects every pattern of 499499 reading errors. Complementary constructions give persistent sign prescriptions, uniform laws for the active coordinates of words, and compact multiplicative subgroups whose nontrivial quotients have positive quadratic entropy. The word-law and entropy conclusions are not asserted for the polynomially decoded encoding. A separate matrix construction gives another causal positive-rate map. Fixed-scale balancing provides an unconditional quarter-sign bound against every small-prime assignment. All coding statements concern deliberately constructed families, not recovery of an arbitrary multiplicative source. The sparse constructions at superlinear cutoffs keep their large-factor mass input explicit.

Provenance statement

The following declaration describes the research and the published source edition: GPT-6 Astra assisted the research notes and initial consolidation; GPT-6 Astra Pro assisted the subsequent proof exposition, reviews, code and finite checks, bibliography and manuscript revisions. The author is responsible for the article. No complete independent human verification, Lean formalisation or independent external replication is claimed. The implementation appendix identifies the reproducible finite checks and their limits; they do not establish the stated large-factor mass hypothesis. For this Hexagon deposit, OpenAI Codex assisted source packaging, compilation checks and the preparation of submission metadata. This preparation does not constitute independent mathematical peer review. The published edition used for this deposit, including its supplementary source material, is available at https://doi.org/10.5281/zenodo.23319574. At the time of preparation for Hexagon, the submitter declares the text primarily AI-generated and declares that no part of the contribution has been checked and understood by a human.

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Version history

  1. v1Submitted by Brice PoulyInitial depositCurrentOct 18, 2026