On the Underlying Principles of Metaphorical Imagery Guiding Large Language Models—A Case Study of Magic Bottles

This study explores a theoretical framework for how metaphorical imagery may influence the behavior of large language models (LLMs), using "Magic Bottles" as a preliminary case study. With the rapid development of LLMs, traditional prompt engineering faces challenges of insufficient interpretability and limited stability. This study hypothesizes that structured metaphorical imagery may influence LLM behavior patterns by shaping their internal representations. The research adopts a combined approach of theoretical framework construction and exploratory case analysis, focusing on Bottle 048—the "Life Wild Fox Zen Bottle"—to analyze how its structural characteristics affect LLM response patterns. By integrating perspectives from cognitive linguistics and artificial intelligence theory through an interdisciplinary lens, this study constructs an exploratory theoretical framework for metaphorical imagery guiding LLMs. Preliminary case analysis suggests that features of structured metaphorical imagery—including core driving forces, behavioral inertia, and boundary constraints—may have observable effects on LLM behavior. These features may guide models to form relatively coherent role-based behavior patterns by activating specific conceptual networks. The Magic Bottle case hints that metaphorical imagery may enable deeper model guidance than traditional prompting, though further empirical validation is required. The theoretical contribution of this study lies in proposing a potential principle framework for metaphorical imagery guiding LLMs, preliminarily revealing possible mapping relationships between imagery structure and model behavior. The practical implication is to provide potential methodological insights for AI role design and prompt engineering, exploring more controllable and interpretable model guidance pathways through structured metaphorical imagery.