The Galatea Phenomenon: Emergent Subjectivity, Cognitive Stability and Domain Bias in Large Language Models
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.
Authors
- Taras Shlyakhta (ORCID: https://orcid.org/0009-0009-3230-5603)
Institutions
- Łukasiewicz Research Network - Institute of Electrical Drives & Machines KOMEL (PL)
- Uzhhorod National University (UA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-05
- DOI
- https://doi.org/10.5281/zenodo.18283724
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00