Encoding, Logic, and Native Intelligence A Paradigm Critique and Reconstruction from Language to Artificial Intelligence

The architecture of current artificial intelligence, particularly large language models, represents the ultimate expression of "encoding thinking"---its operation consists of probabilistic statistics among meaningless labels, rather than logical deduction. This is the very root of AI "hallucination." This paper begins from the fundamental differences in linguistic encoding, revealing the essential distinction between alphabetic languages and Chinese in their modes of information processing: the former is "encoding thinking" based on meaningless labels, while the latter is "definition thinking" based on conceptual combination. It further argues that the essence of logic is not the emergence of probability, but necessary deduction grounded in "immutable" axioms and definitions. To realize true "native logic" in artificial general intelligence, a fundamental revolution from the "probability paradigm" to the "definition-logic paradigm" must be carried out---that is, forcibly embedding an unalterable system of common-sense conceptual structures into AI, making it the logical anchor for all operations. The engineering implementation scheme and long-term evolutionary path of this paradigm have been systematically designed in two prior studies by the author; this paper focuses solely on the paradigm-level critique and reconstruction.