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Autore principale: Shigemura, Toshiyuki
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2512.23716
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author Shigemura, Toshiyuki
author_facet Shigemura, Toshiyuki
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_23716
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Noise-Driven Persona Formation in Reflexive Neural Language Generation
Shigemura, Toshiyuki
Computation and Language
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.
title Noise-Driven Persona Formation in Reflexive Neural Language Generation
topic Computation and Language
url https://arxiv.org/abs/2512.23716