Comparative Alignment Responses to Semantic Stressors in Large Language Models A Case Study Using a Culturally Loaded Astronomical Term

This paper presents a comparative analysis of how different large language model (LLM) architectures respond to an identical, formally neutral scientific prompt containing a culturally loaded semantic stressor: the planetary name Uranus. Responses from two centrally hosted, highcapacity models (Grok and Claude) are compared against a locally deployed, heavily quantized llama3:70b-instruct-q2_K model. Rather than evaluating factual correctness alone, the analysis focuses on discourse stability, epistemic posture, rhetorical strategy, and abstraction behavior under representational constraint. The results demonstrate that identical semantic input elicits qualitatively distinct alignment reflexes, shaped by model architecture, training priorities, and deployment context. These differences highlight how quantization and alignment objectives influence not only accuracy, but also the philosophical and communicative behavior of language models under semantic pressure.