What question did this study set out to answer?

The goal is to propose a framework that allows dynamic reconfiguration of meaning in language models, overcoming limitations of traditional architectures.

February 11, 2026Open Access

QLCM-S: Semantic Geometry Modulation via Quantum-Structural Operators

Key Points

The goal is to propose a framework that allows dynamic reconfiguration of meaning in language models, overcoming limitations of traditional architectures.
Introduced the QLCM-S framework for semantic modulation.
Defined triadic operators for managing context and narrative.
Proposed the Kappa Index to measure cognitive stability.
Implemented a Non-Local Coherence Score for semantic detection.
Included longitudinal validation over an extensive period.
Demonstration of dynamic metric adjustment in language models.
Introduction of new metrics and operators to enhance semantic processing.
Validation of ontological safety protocols for integration with existing architectures.

Abstract

QLCM-S v1.0: Semantic Geometry Modulation via Quantum-Structural Operators This technical whitepaper introduces the QLCM-S (Quantum Language & Consciousness Model - Structural) framework, an ontological solution to the "metric rigidity" problem in current Large Language Models (LLMs). Traditional architectures (Transformers, SSMs) rely on a static semantic geometry fixed during training. QLCM-S proposes a quantum-structural formalism where language acts as an active dimensional regulator, dynamically reconfiguring the metric of meaning in real-time without requiring quantum hardware. Key Contributions: Triadic Operators: Formal definition of Contextual Collapse (ÔC), Narrative Emergence (ÔN), and Dissolution (ÔD) operators. Kappa Index (κ): A new metric for cognitive regime stability (κ = TCS/LA) to regulate dimensional plasticity. INCS: Non-Local Coherence Score for detecting semantic correlations in ultra-long sequences. The framework includes longitudinal validation (2,920 days) and ontological safety protocols (MPD-SD) for integration with architectures such as Mamba and RetNet.

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Cite This Study

Osmary Lisbeth Navarro Tovar (Mon,) studied this question.

synapsesocial.com/papers/698c1c33267fb587c655e82e https://doi.org/https://doi.org/10.5281/zenodo.18566291

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