人工智能架构中普适的演化统计特征

摘要

我们通过检验人工智能架构演化是否遵循与生物演化相同的统计规律，基于161篇文献中的935项消融实验发现：架构修改的适应度效应分布（DFE）呈现重尾型学生t分布，其中主要架构消改的效应比例（有害68%、中性19%、有益13%，样本量n=568）使人工智能处于紧凑型病毒基因组与简单真核生物之间的演化区间。该分布形态与黑腹果蝇（标准化KS=0.07）和酿酒酵母（KS=0.09）高度吻合；而有益突变比例显著高于生物界（13%对比1-6%）量化了定向搜索相对盲目搜索的优势，同时保留了分布形态的保守性。架构创新遵循逻辑斯蒂动力学（R²=0.994），呈现间断平衡与向领域生态位自适应辐射的特点，14项架构特征被独立发明3-5次，与生物趋同演化现象形成对照。这些结果表明演化的统计结构具有基质无关性，由适应度景观的拓扑结构而非选择机制决定。

English

We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy-tailed Student's t-distribution with proportions (68% deleterious, 19% neutral, 13% beneficial for major ablations, n=568) that place AI between compact viral genomes and simple eukaryotes. The DFE shape matches D. melanogaster (normalized KS=0.07) and S. cerevisiae (KS=0.09); the elevated beneficial fraction (13% vs. 1-6% in biology) quantifies the advantage of directed over blind search while preserving the distributional form. Architectural origination follows logistic dynamics (R^2=0.994) with punctuated equilibria and adaptive radiation into domain niches. Fourteen architectural traits were independently invented 3-5 times, paralleling biological convergences. These results demonstrate that the statistical structure of evolution is substrate-independent, determined by fitness landscape topology rather than the mechanism of selection.