基于固定语义锚点与分层变换函数的AI原生认知架构——脱离统计拟合的类人理解范式

Abstract

Current mainstream artificial intelligence models including convolutional neural networks and large language models rely on statistical fitting over fragmented input symbols. Vision models fit pixel distributions, while language models predict next‑token probabilities. This paradigm is essentially pattern‑matching and probabilistic speculation rather than genuine semantic understanding, and it inherently produces hallucinations, semantic drift, long‑tail failures and uncontrolled emergence. Breaking away from statistical‑learning frameworks, this paper proposes an AI‑native cognitive dynamical architecture built upon read‑only fixed semantic anchors. Instead of adopting human surface‑level grammar as internal computation rules, we construct a stack of layered transformation functions together with a graded semantic‑matching validation mechanism. Without token‑level probability sampling or massive pre‑training, five‑step cognitive dynamics (anchor assembling, verb stacking, complement modification, word‑order rearrangement and equivalent word replacement) reproduce core human‑like language comprehension and generation. A small‑scale sandbox experiment with ten basic semantic anchors demonstrates that the architecture eliminates root‑cause hallucinations and semantic drift, featuring full traceability, low computational cost and strong generalization. It represents a new human‑like cognitive paradigm alternative to statistical AI. Note: The mathematical dynamical function for semantic coupling, which inherits the prior “glue‑temporal‑grid” hypothesis, is not developed in this paper and will be presented in a follow‑up independent publication. Keywords AI‑native cognition; semantic anchor; transformation‑function stack; AI‑native grammar; statistics‑free modelling; explainable AI; hallucination mitigation