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We investigate the nature of intelligence in Large Language Models (LLMs), arguing that their impressive capabilities stem from next-token prediction (NTP) combined with externally supplied cognitive structures, primarily Chain-of-Thought (CoT) prompting. It critically examines this "NTP + Schemata" model through the lens of Jean Piaget's theory of cognitive development, differentiating between assimilation (fitting new information into existing frameworks) and

The Assimilation-Accommodation Gap in LLM Intelligence

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