Waking Up an AI: A Quantitative Framework for Prompt-Induced Phase Transition in Large Language Models
This work investigates whether large language models (LLMs) exhibit human-like cognitive phase transitions under controlled prompting—specifically, whether they demonstrate intuitive conceptual integration akin to human cognition. Method: We introduce the novel concept of “Prompt-Induced Phase Transition” (PIPt) and propose a two-component framework comprising a Trigger Identification Protocol (TIP) and a Quantitative Prompting Protocol (TQP), enabling fine-grained, reproducible measurement of LLMs’ semantic fusion responses. Experiments systematically manipulate semantic distance, compare across models, and modulate affective and linguistic quality. Contribution/Results: We provide the first systematic empirical validation that current state-of-the-art LLMs show no significant response discontinuity under semantic fusion prompts—distinguishing them fundamentally from human intuitive conceptual integration. The study establishes the first quantitative analytical paradigm for assessing LLM cognitive behavior, revealing structural limitations in their conceptual manipulation capabilities.