🤖 AI Summary
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.
📝 Abstract
What underlies intuitive human thinking? One approach to this question is to compare the cognitive dynamics of humans and large language models (LLMs). However, such a comparison requires a method to quantitatively analyze AI cognitive behavior under controlled conditions. While anecdotal observations suggest that certain prompts can dramatically change LLM behavior, these observations have remained largely qualitative. Here, we propose a two-part framework to investigate this phenomenon: a Transition-Inducing Prompt (TIP) that triggers a rapid shift in LLM responsiveness, and a Transition Quantifying Prompt (TQP) that evaluates this change using a separate LLM. Through controlled experiments, we examined how LLMs react to prompts embedding two semantically distant concepts (e.g., mathematical aperiodicity and traditional crafts)-either fused together or presented separately-by changing their linguistic quality and affective tone. Whereas humans tend to experience heightened engagement when such concepts are meaningfully blended producing a novel concept-a form of conceptual fusion-current LLMs showed no significant difference in responsiveness between semantically fused and non-fused prompts. This suggests that LLMs may not yet replicate the conceptual integration processes seen in human intuition. Our method enables fine-grained, reproducible measurement of cognitive responsiveness, and may help illuminate key differences in how intuition and conceptual leaps emerge in artificial versus human minds.