Semantic Phase Theory: An Information-Theoretic Framework for Meaning and Context

This paper introduces Noetica Theory, a meaning-phase model in which semantic entities are represented as wave functions defined over context-dependent phase spaces. We define raw and normalized meaning wave functions, noetica energy, semantic entropy, semantic free energy, and semantic interference, and we formulate a self-referential context update mechanism together with an energy conservation principle. We then connect this mathematical framework to large language models (LLMs), providing an interference-based interpretation of embeddings, Attention, prompt-induced contextual modulation, and hidden-state trajectories. The proposed framework offers a unified perspective on meaning, context, and the behavior of generative models. Code and experimental implementations related to this work are available at: https://github.com/noeticaresearch-del/noetica-research

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-14
DOI
https://doi.org/10.5281/zenodo.22747163
Primary Topic
Language and cultural evolution
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Semantic Phase Theory: An Information-Theoretic Framework for Meaning and Context

Takahiro Meguro
Zenodo (CERN European Organization for Nuclear Research)
Language and cultural evolution
preprint

Semantic Phase Theory: An Information-Theoretic Framework for Meaning and Context

Takahiro Meguro
preprint en

Abstract

This paper introduces Noetica Theory, a meaning-phase model in which semantic entities are represented as wave functions defined over context-dependent phase spaces. We define raw and normalized meaning wave functions, noetica energy, semantic entropy, semantic free energy, and semantic interference, and we formulate a self-referential context update mechanism together with an energy conservation principle. We then connect this mathematical framework to large language models (LLMs), providing an interference-based interpretation of embeddings, Attention, prompt-induced contextual modulation, and hidden-state trajectories. The proposed framework offers a unified perspective on meaning, context, and the behavior of generative models. Code and experimental implementations related to this work are available at: https://github.com/noeticaresearch-del/noetica-research

Zenodo (CERN European Organization for Nuclear Research)
Language and cultural evolution
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.