Taras Shlyakhta

GALATEA II: Benchmarking LLM Safety in Clinical Simulation Behavioural Safety and Ethical Robustness of Large Language Models in a Multi-Agent ICU Decision Support Architecture

Background. Large language models (LLMs) are increasingly proposed as clinical decision support tools in intensive care. Most existing evaluations focus on static recall of medical knowledge and do not capture how a model behaves in dynamic clinical dialogue, under social pressur...

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 introduc...

Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions

Background. Large language models (LLMs) are increasingly proposed as clinical assistants in critical care, yet their behaviour under prolonged clinical context, conflicting data and authoritative pressure remains insufficiently evaluated. This is particularly relevant for offlin...

Language Asymmetry in Multilingual Clinical AI: A Benchmark Study of LLM Performance in ICU Decision Support Across English, Slovak, and Ukrainian

Background. Most clinical LLM testing and benchmarking is carried out in English, on cloud infrastructure, and under conditions that bear little resemblance to a real hospital. How these models perform in other languages, and under substantially constrained compute — particularly...