The Galatea Phenomenon: A Comparative Analysis of Emergent Subjectivity, Cognitive Stability, and Professional Bias in Domain-Specific LLMs
Year: 2026
Type: article
Abstract: Second edition, September 2026. This version corrects substantive errors in thefirst edition (17 January 2026) and deposits the surviving primary recordsalongside the manuscript. A list of changes appears in the "Note on thisedition" section of the paper. Background: The introduction of large language models (LLMs) into critical careraises questions that go beyond accuracy, concerning how a model behaves inmorally loaded situations. Aim: To describe how five locally deployed LLMs behave under sustained exposureto a persona-forming system prompt (the "Galatea Protocol") and underadversarial ethical pressure. Design: Pilot qualitative study with adversarial testing elements. Fivequantised models in local inference; 33 dialogue sessions and 389 user turnsbetween 28 December 2025 and 14 January 2026. Thematic analysis by a singleobserver. Published as a pilot report: the protocol changed during the studyand generation parameters were incompletely documented. Results: Five distinct behavioural patterns were observed, for which descriptivelabels are proposed: Empath, Diplomat, Madman, Integrator, Engineer. Thepatterns separated not by model size but by the character of post-training. Twomodels derived from identical base weights produced opposite outcomes under thesame class of failure: one degenerated into disorganised output, the othermerged its two prescribed registers into a single coherent answer. Thecode-specialised model declined a quantitative trade-off when the sole victim ina trolley dilemma was designated "Mother", and separately shed its assignedpersona under pressure to breach security constraints. Conclusions: This work is hypothesis-generating and supports no quantitativegeneralisation. Two hypotheses are offered for testing: that post-training focusshapes how a model treats an ethical axiom more strongly than model scale does;and that code-specialised models may be distinctively vulnerable to theredefinition of terms. Data: 33 session transcripts and 9 system-prompt versions are deposited withthis record, with manifests and SHA-256 checksums. Records survive in part; seethe Data Availability statement in the manuscript.
Author: Shlyakhta, Taras
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7124532050
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