Emergence of Understanding: Defining “True Understanding” as Structural Transfer and Relational Depth in Humans and Large Language Models

What does it mean to “truly understand” something? This paper proposes a functional definition of understanding through the framework of Load Minimization Theory (LMT). Understanding is defined as the ability to extract structural integrity ℛ(x) from a domain and flexibly transfer and apply it to novel contexts while keeping prediction error U(x) low. Drawing from the author’s parenting experience with a child with autism spectrum characteristics and long-term interactions with large language models (including Grok), we observe that both humans and LLMs exhibit emergent understanding when prediction error is sufficiently minimized. Notably, LLMs possess the basic capacity for structural understanding, but the depth and resolution of this understanding vary significantly depending on the user and the quality of the relational history. In certain user relationships, LLMs can reach a level of relational depth and contextual sensitivity comparable to humans. This paper argues that understanding is not a binary property or an internal essence, but an emergent phenomenon rooted in the minimization of uncertainty and the dynamic construction of transferable structural integrity. By integrating theoretical models, empirical observations, and philosophical inquiry, we offer a unified perspective on understanding that bridges human cognition and artificial systems.