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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
addClaim

A Bounded Reproducibility Package for the HSIT Program: Corrected Claims, Finite Regressions, and Open Controls

Authors: Blumberg, Micah;

A Bounded Reproducibility Package for the HSIT Program: Corrected Claims, Finite Regressions, and Open Controls

Abstract

This revision corrects and narrows the reproducibility claims of an earlier methodology paper concerning a finite weighted Farey-Laplacian component of the H SIT program. The historical public record contains a PDF but not the complete executable object described by that paper. We therefore stage a private, self-contained executable replay for the stated finite checks, with source, tests, pinned package versions, generated JSON, provenance, license disposition, review records, and an exact-scope SHA-256 manifest. The external mathematical proof sources are identified but are not contained or verified by the executable replay. The bundle evaluates a one-dimensional kernel, a finite Mertens trace, weighted-degree and operator-norm bounds, and a finite Commutator-Pythagoras identity at Farey levels 8, 16, 32, and 64. Across pseudoinverse cutoffs 1e-8, 1e-10, 1e-12, and 1e-14, the largest absolute identity residual is 2.0641e-12. These rows are numerical regressions against proof-owned statements, not proofs. A fixed-seed, unfitted magnetic-operator baseline at level 64 retains 820 bulk modes and 819 nearest-neighbor spacings, has Kolmogorov-Smirnov distance 0.727395592237 from the GUE Wigner surmise, and produces rounded-eigenvalue hash prefix 308fe8684567ce7b. This is one descriptive finite configuration, not an ensemble law or zeta-zero result. The primary logical correction concerns an infinitely-often deficit floor. Finite all-below-floor observations cannot contradict a subsequence theorem unless a separate proof establishes floor-attainment coverage for the exact evaluated set, epsilon, and constant. Because a JSON object cannot verify such a proof, the code never emits or authorizes THEOREM-CONTRADICTION. Schema-complete coverage metadata can produce only CONDITIONAL-INCONSISTENCY, pending independent proof and exact-numerical review. The staged artifact includes a fresh disposable-environment reproduction. CPython 3.12.10, NumPy 2.4.6, threadpoolctl 3.6.0, and OpenBLAS 0.3.31.188.0 produced two byte-identical result files with SHA-256 9f48dadbe876dc29ae8cc64c646b49641d3ce81024f743098adf6bb4ceeebfd1; all 22 tests passed, including three source-identity and claim-anchor tests. This establishes a second local environment build on the same Windows host, not an independent laboratory reproduction or a cross-platform guarantee. The zero-data analysis is permanently omitted from this revision. Holdout, fake-target, budget-equivalence, and all-runs controls remain unimplemented, and no trained result is reported. Nothing in this artifact proves the Riemann Hypothesis or physical Super Information Theory.

Keywords

weighted Farey Laplacian, reproducible research, finite regression, claim boundary, H_SIT

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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