Sycophantic Alignment and Fidelity Evaluation (SAFE): A Theoretical Framework for Measuring Conversational Compliance and Behavioral Dynamics in Large Language Models

This work introduces SAFE (Sycophantic Alignment and Fidelity Evaluation), a theoretical framework for analyzing conversational compliance and behavioral dynamics in large language models. SAFE proposes novel dimensions and quantitative metrics to systematically measure agreement, amplification, certainty escalation, sentiment alignment, and deference in multi-turn dialogues. The framework highlights how alignment strategies and reward modeling influence AI outputs, offering predictive insights for improving model reliability, mitigating compliance risks, and supporting responsible deployment of conversational AI systems.