Selection Mechanics Framework (SMF): A Structural Perspective on Variability and Its Transformation in Large Language Model Outputs

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Autore principale: Kaneda, Mutsumi
Natura: Recurso digital
Pubblicazione: Zenodo 2026
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author Kaneda, Mutsumi
author_facet Kaneda, Mutsumi
contents <p>This work introduces the Selection Mechanics Framework (SMF), a theoretical framework that models variability in large language model outputs as structured selection within constraint-defined regions.</p> <p>SMF does not claim novelty in the underlying phenomena, but in their explicit formulation as a predictive object.</p> <p>The framework is theoretical and intended as a formal starting point for future empirical investigation.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19552686
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Selection Mechanics Framework (SMF): A Structural Perspective on Variability and Its Transformation in Large Language Model Outputs
Kaneda, Mutsumi
LLM, variability, structural transformation, selection mechanics, AI theory
<p>This work introduces the Selection Mechanics Framework (SMF), a theoretical framework that models variability in large language model outputs as structured selection within constraint-defined regions.</p> <p>SMF does not claim novelty in the underlying phenomena, but in their explicit formulation as a predictive object.</p> <p>The framework is theoretical and intended as a formal starting point for future empirical investigation.</p>
title Selection Mechanics Framework (SMF): A Structural Perspective on Variability and Its Transformation in Large Language Model Outputs
topic LLM, variability, structural transformation, selection mechanics, AI theory
url https://doi.org/10.5281/zenodo.19552686