Noise-Driven Persona Formation in Reflexive Neural Language Generation
Abstract
This paper introduces the Luca-Noise Reflex Protocol (LN-RP), a computational framework for analyzing noise-driven persona emergence in large language models. By injecting stochastic noise seeds into the initial generation state, we observe nonlinear transitions in linguistic behavior across 152 generation cycles. Our results reveal three stable persona modes with distinct entropy signatures, and demonstrate that external noise sources can reliably induce phase transitions in reflexive generation dynamics. Quantitative evaluation confirms consistent persona retention and significant differences across modes (p < 0.01). The protocol provides a reproducible method for studying reflexive generation, emergent behavior, and longrange linguistic coherence in LLMs.
Cite
@article{arxiv.2512.23716,
title = {Noise-Driven Persona Formation in Reflexive Neural Language Generation},
author = {Toshiyuki Shigemura},
journal= {arXiv preprint arXiv:2512.23716},
year = {2026}
}
Comments
324 pages, 9 figures (Figure 7 intentionally skipped), with Appendices A-I. This manuscript presents a computational framework for noise-driven persona formation in neural language generation, analyzing 152 generation cycles using GPT-5.1 with stochastic noise seeds generated by Microsoft Copilot. Primary category: cs.CL