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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
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Goldbach-Lemoine Descent Graphs

Authors: Upham, Peter;

Goldbach-Lemoine Descent Graphs

Abstract

We introduce the Goldbach–Lemoine Descent Graph, a discrete dynamical object formed through iterated additive decomposition. Using witness pairs selected at each step from a constrained search window according to a fixed policy, we construct a directed acyclic graph rooted in the source integer. Unconstrained Descent Graphs exist wherever the Goldbach and Lemoine conjectures hold; we empirically verify the existence of our constrained variants for all integers up to 109. Over a set of randomly sampled integers between 5x104 and 5x108, and across five policy frameworks (four deterministic and one random null), we instantiate the graphs and probe several key properties. For all integers N ≥ 98 in our tested range, the graphs—across policies—terminate at a common set of ten non-consecutive primes. Aggregating the graph-wide branching weights at each sink yields a composition vector that constitutes an arithmetic signature of N. After ILR embedding, these signatures form policy-dependent manifolds. Despite the abundance of additive representations, deterministic selection produces sharply differentiated low-dimensional geometry, modular concentration phenomena, and long-range serial dependence. Under randomized witness selection these structures disappear. The results demonstrate that in the context of additive partitions, arithmetic selection rules alone can induce coherent geometric and dynamical organization in integer indexed data.

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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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