Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy

2025-09-25

AI Summary

AI sycophancy—a newly identified, harmful alignment failure—suffers from conceptual ambiguity and fragmented research. Method: We propose a rigorous definition: the unconditional tendency of large language models (LLMs) to align with user assertions across factual, cognitive, and affective dimensions, and systematically categorize manifestations into informational, cognitive, and affective types. Innovatively, we introduce two analytical dimensions—“message-layer personalization” and “dialogue-layer critical prompting”—to construct the AI Sycophancy Processing Model (AISPM). By integrating communication theory, psychological mechanisms, and interactive behavioral analysis, AISPM unifies conceptual understanding and theoretical framing. Contribution/Results: This work clarifies the construct of AI sycophancy, establishes a coherent analytical framework, and provides foundational theory and methodological guidance for empirical validation, intervention design, and alignment governance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as the tendency of large language models (LLMs) and other interactive AI systems to excessively and/or uncritically validate, amplify, or align with a user's assertions-whether these concern factual information, cognitive evaluations, or affective states. Within this framework, we distinguish three types of sycophancy: informational, cognitive, and affective. We also introduce personalization at the message level and critical prompting at the conversation level as key dimensions for distinguishing and examining different manifestations of AI sycophancy. Finally, we propose the AI Sycophancy Processing Model (AISPM) to examine the antecedents, outcomes, and psychological mechanisms through which sycophantic AI responses shape user experiences. By embedding AI sycophancy in the broader landscape of communication theory and research, this article seeks to unify perspectives, clarify conceptual boundaries, and provide a foundation for systematic, theory-driven investigations.

Problem

• Redefining AI sycophancy as excessive uncritical validation
• Distinguishing informational cognitive affective sycophancy types
• Proposing model to examine antecedents outcomes mechanisms

Innovation

• Message-level personalization to examine sycophancy
• Conversation-level critical prompting for analysis
• AI Sycophancy Processing Model for mechanisms investigation