From Differential Seeds to Integral Meaning: A Mathematical Theory of Semantic Integration in Neural Language Models

We present a comprehensive mathematical framework that reinterprets neural language generation as a structured integration process over semantic meaning fields. Our central thesis establishes that prompt tokens function as differential initiators (∂M/

Authors

Do-Geun Kim (ORCID: https://orcid.org/0000-0002-0066-884X)

Institutions

Korea Brain Research Institute (KR)

Publication Details

Published: 2025-08-18
DOI: https://doi.org/10.36227/techrxiv.175554757.76254349/v1
Primary Topic: Neural Networks and Applications
Type: article
Field-Weighted Citation Impact: 0.00

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article

From Differential Seeds to Integral Meaning: A Mathematical Theory of Semantic Integration in Neural Language Models

Do-Geun Kim

Aug 18, 2025

Neural Networks and Applications

article

From Differential Seeds to Integral Meaning: A Mathematical Theory of Semantic Integration in Neural Language Models

Do-Geun Kim

August 18, 2025article en

Abstract

Korea Brain Research Institute (KR)

Reduced inequalities

Openalex Percentile: Top 89%

Neural Networks and Applications

DOI Open Access PDF OpenAlex

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From Differential Seeds to Integral Meaning: A Mathematical Theory of Semantic Integration in Neural Language Models — Do-Geun Kim · (2025) | TGRS Research Map | TGRS