Ontology of Intelligence

Abstract

Existing Transformer and large model theoretical frameworks uniformly adopt the “predictionfirst” paradigm, defining intelligence as fitting the data distribution of the real world and next-token prediction. This theory overturns the conventional view and proposes the sole origin of intelligence: continuous, multi-layered, global closed-loop mathematical verification. All artificial intelligence behaviors (fitting, reasoning, association, generation, generalization, error correction, cognition) are essentially mathematical comparison, constraint checking, differential stability verification, and vector structure matching at different dimensions. All low-level operations of the traditional Transformer (vector embedding, QKV dot-product attention, gradient backpropagation, distribution fitting) are not “predictive operations” but native statistical-level checking operations. Hallucinations, logical failures and generalization defects of large models fundamentally arise from relying only on statistical distribution checking while lacking rigorous mathematical verification at the axiomatic, differential constraint and geometric topological levels. This theory constructs a two-tier unified intelligence system based on checking, completing the fundamental paradigm shift from traditional probabilityfitting intelligence to global mathematical-verification intelligence. It can directly guide the design of next-generation native intelligent models with low hallucination, strong logic, self-correction capability and ontological cognition.

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