Anchor-Pure Reconstruction of Regular Tetration: Riemann–Hilbert Selection, Analytic Base Flow, and Blind Validation

We develop an anchor-pure method for reconstructing the regular Kneser tetration at a target base d > e^(1/e) from a single known anchor at base e. No tetration, superlogarithm, hub, or response-kernel data at the target base are used in the construction. The key representation decomposes the target tetration into a raw branch obtained by stopped logarithmic peeling and a periodic degree-one correction. We derive the exact base derivative of the raw branch, prove cancellation of the nonperiodic tails in the hub equation, select the base-response field through a Riemann–Hilbert problem, and obtain a closed periodic evolution on a fixed weighted analytic path space. We also develop a finite certification framework based on contraction bounds, segment composition, two-sided fixed-point gluing, and cut-plane continuation. Two independent numerical architectures validate the reconstruction at base 2. A 65-node global base flow reconstructs the regular tetration at base 2 with a blind pointwise discrepancy of 4.0 × 10^−21 on 48 off-grid points and resolves the periodic hub to approximately 30.6 decimal digits. An independently implemented ten-segment re-anchoring march agrees with both the first reconstruction and an independently computed target-base reference to 7.6 × 10^−11. The observed failure and successful subdivision of the final edge segment agree with the predicted Ovsyannikov step-size regime. These experiments establish a reproducible reconstruction method and validate its internal error predictors. The accompanying certification framework is rigorous, while completion of the full interval march remains an open computer-assisted problem.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-30
DOI
https://doi.org/10.5281/zenodo.21709404
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

Anchor-Pure Reconstruction of Regular Tetration: Riemann–Hilbert Selection, Analytic Base Flow, and Blind Validation

Janis Justus
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

Anchor-Pure Reconstruction of Regular Tetration: Riemann–Hilbert Selection, Analytic Base Flow, and Blind Validation

Janis Justus
preprint en

Abstract

We develop an anchor-pure method for reconstructing the regular Kneser tetration at a target base d > e^(1/e) from a single known anchor at base e. No tetration, superlogarithm, hub, or response-kernel data at the target base are used in the construction. The key representation decomposes the target tetration into a raw branch obtained by stopped logarithmic peeling and a periodic degree-one correction. We derive the exact base derivative of the raw branch, prove cancellation of the nonperiodic tails in the hub equation, select the base-response field through a Riemann–Hilbert problem, and obtain a closed periodic evolution on a fixed weighted analytic path space. We also develop a finite certification framework based on contraction bounds, segment composition, two-sided fixed-point gluing, and cut-plane continuation. Two independent numerical architectures validate the reconstruction at base 2. A 65-node global base flow reconstructs the regular tetration at base 2 with a blind pointwise discrepancy of 4.0 × 10^−21 on 48 off-grid points and resolves the periodic hub to approximately 30.6 decimal digits. An independently implemented ten-segment re-anchoring march agrees with both the first reconstruction and an independently computed target-base reference to 7.6 × 10^−11. The observed failure and successful subdivision of the final edge segment agree with the predicted Ovsyannikov step-size regime. These experiments establish a reproducible reconstruction method and validate its internal error predictors. The accompanying certification framework is rigorous, while completion of the full interval march remains an open computer-assisted problem.

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
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Anchor-Pure Reconstruction of Regular Tetration: Riemann–Hilbert Selection, Analytic Base Flow, and Blind Validation — Janis Justus · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS