Changing the Ruler to Measure AI: The Statistical Fitting Dilemma and Topological Endogenous Breakthrough from the RLI Perspective

Abstract:The current evolution of AI remains locked within the statistical fitting paradigm dominated by Transformers. This paper steps outside the deep learning framework and introduces the physical and geometric ontological perspective of 'Regional Locking Inference' (RLI). Mainstream AI lacks endogenous perception of topological tension; true intelligence emergence arises from the coupling of local relationship locking (A1) and macroscopic topological phase transitions (T6). We present a 2026 frontier comparison, highlighting RLIAI's gradient-free learning driven by graph Laplacian spectral gaps and forced topological reconstruction. 摘要:当前人工智能的演进依然被锁定在以 Transformer 为主导的统计拟合范式内。本文跳出深度学习框架,引入“区域锁定推断”(RLI)的物理与几何本体论视角。主流 AI 缺乏对网络拓扑张力的内生感知;真正的智能涌现应来自局部关系锁定(A1)与宏观拓扑相变(T6)的耦合。文章对比了 2026 年前沿架构,突出了 RLIAI 依靠图拉普拉斯谱间隙驱动的无梯度学习与强制拓扑重构机制。