Noise-Driven Persona Formation in Reflexive Neural Language Generation

TL;DR

This paper presents a novel framework for analyzing how noise influences persona emergence in large language models, revealing stable modes and phase transitions in linguistic behavior through a reproducible protocol.

Contribution

Introduces the Luca-Noise Reflex Protocol (LN-RP), a new method for studying noise-driven emergent personas and phase transitions in neural language generation.

Findings

Identifies three stable persona modes with distinct entropy signatures.

Demonstrates external noise can induce phase transitions reliably.

Confirms consistent persona retention across generation cycles.

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.