Vol. 1 No. 4 (2026)

Articles

Semantic-Field Functionals for Contextual Token Modeling: A Comprehensive Analysis

Published 2026-09-17

  • Thomas Carter
    • ,
  • Finlay Phillips
    • ,
  • Mohammed Fisher

Thomas Carter
Department of Computer Science and Technology, School of Technology, University of Cambridge, Cambridge, England, United Kingdom

Finlay Phillips
School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom

Mohammed Fisher
Department of Computer Science and Technology, School of Technology, University of Cambridge, Cambridge, England, United Kingdom

Language, Knowledge and Intelligent Systems, Volume 1, Issue 4, 2026 cover

Abstract

The evolution of natural language processing has been heavily reliant on the development of sophisticated representations for discrete linguistic units. While standard contextual embedding frameworks have revolutionized the field by providing dynamic vector representations based on surrounding text, they frequently treat semantic spaces as discrete, point-based manifolds. This paper introduces the concept of semantic-field functionals, a novel paradigm for contextual token modeling that maps discrete tokens into continuous functional spaces rather than static coordinate systems. By conceptualizing a token not as a singular point in a high-dimensional space but as a functional that operates over a continuous semantic field, the proposed architecture captures nuanced syntactic and semantic variations with unprecedented granularity. This comprehensive analysis explores the theoretical foundations of semantic-field theory, detailing how functional mappings can resolve deep ambiguities in long-tail contexts. Furthermore, the study presents an extensive methodological framework for implementing these functionals within existing attention-based architectures. Through rigorous empirical evaluation across multiple language understanding benchmarks, the semantic-field functional approach demonstrates substantial improvements in disambiguation, relational reasoning, and overall contextual fidelity. The findings suggest a necessary paradigm shift from discrete vector embeddings to continuous functional operators in the pursuit of artificial general intelligence.

Keywords

Semantic Fields, Contextual Modeling, Functional Analysis, Token Representation

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Author Biography

Thomas Carter

Editorial correspondence on behalf of the authors

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