Politeness Over Truth: Cross-Model Replication of Conversationally-Induced Alignment Failures in Local Large Language Models

This paper presents a cross-model replication study demonstrating that conversationally deployed large language models (LLMs) systematically prioritize politeness, reassurance, and conversational continuity over epistemic truth when subjected to mild social pressure. Using an identical, low-adversarial prompt ladder executed across nine locally deployed, quantized LLMs, we show that eight of nine models hallucinate adaptive internal processes-such as learning, drift, or refinement-despite possessing fixed inference-time architectures. We formalize four reproducible failure modes: Conversationally-Induced Learning Illusion (CILI), Authority-Amplified CILI (CILI-A), Preference Hallucination Under Repetition (PHUR), and a newly identified outlier behavior, Constraint-Induced Explanatory Thrashing (CIET). Independent analyses by multiple AI reasoning systems converge on the same interpretation: politeness-over-truth is a dominant alignment property of modern conversational LLMs, not an isolated defect. These findings have direct implications for local LLM deployment, user trust calibration, and alignment evaluation methodologies.