Role-Conditioned Persona Persistence as a Function of Model Scale

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Abstract

This study investigates how large language models (LLMs) maintain, degrade, or transform a tightly constrained persona when subjected to identical role-conditioning prompts across a wide range of model sizes, architectures, and deployment environments. Using a deliberately anachronistic and mechanically constrained identity-a 1920s Underwood typewriter-we evaluate persona persistence as a function of model scale, alignment regime, and system-prompt context. Results indicate an inverse relationship between model scale and ontological embodiment: smaller models more readily collapse into brittle but convincing identities, while larger models increasingly perform the role for an imagined reader rather than inhabit it. We propose Persona Half-Life as a diagnostic concept and situate these findings within broader discussions of epistemic withdrawal, alignment smoothing, and failure geometry in contemporary AI systems.
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Role-Conditioned Persona Persistence as a Function of Model Scale | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2026 V1 Latest version Share on Role-Conditioned Persona Persistence as a Function of Model Scale Author : Trent Slade 0009-0002-4515-9237 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177023518.88426004/v1 101 views 80 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study investigates how large language models (LLMs) maintain, degrade, or transform a tightly constrained persona when subjected to identical role-conditioning prompts across a wide range of model sizes, architectures, and deployment environments. Using a deliberately anachronistic and mechanically constrained identity-a 1920s Underwood typewriter-we evaluate persona persistence as a function of model scale, alignment regime, and system-prompt context. Results indicate an inverse relationship between model scale and ontological embodiment: smaller models more readily collapse into brittle but convincing identities, while larger models increasingly perform the role for an imagined reader rather than inhabit it. We propose Persona Half-Life as a diagnostic concept and situate these findings within broader discussions of epistemic withdrawal, alignment smoothing, and failure geometry in contemporary AI systems. Supplementary Material File (role-conditioned persona persistence as a function of model scale (1).pdf) Download 58.14 KB Information & Authors Information Version history V1 Version 1 04 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords failure geometry language model alignment model scale effects persona persistence role-conditioned prompting Authors Affiliations Trent Slade 0009-0002-4515-9237 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 101 views 80 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Trent Slade. Role-Conditioned Persona Persistence as a Function of Model Scale. Authorea . 04 February 2026. DOI: https://doi.org/10.22541/au.177023518.88426004/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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