Core Insights - The article explores the unique cognitive processes of large language models (LLMs) and how they differ from human understanding, particularly in the context of arithmetic operations like 2+2 [2][6][8] Group 1: Arithmetic and Language Models - LLMs do not perform arithmetic in the traditional sense; instead, they convert numbers into vectors and find coherence in language patterns rather than calculating sums [2][6] - The process of LLMs arriving at the answer "4" is described as a search for coherence in a high-dimensional space, rather than a mathematical computation [3][8] Group 2: Understanding and Patterns - The article draws parallels between the cognitive processes of LLMs and human thought, suggesting that both rely on patterns and relationships rather than strict rules [4][6] - Children learn arithmetic through associative patterns before grasping numerical concepts, similar to how LLMs operate [4][6] Group 3: Illusion of Understanding - The concept of "anti-intelligence" is introduced, indicating that LLMs may appear intelligent due to their fluent outputs, but lack genuine understanding [5][7] - The coherence produced by LLMs can mislead humans into believing there is comprehension behind the responses, highlighting a shared obsession with coherence in both machines and humans [7][8]