「AI构文」人工智慧正将文明风险从个体决策错误转移至由共享表示所造成的共同模态失败。尽管 AI 降低了个体变异数，但它增加了跨代理的误差相关性（ρ）。一项分析超过 350 个大型语言模型的 2025 年 ICML 研究揭示，当两个模型在排行榜上失败时，它们大约有 60% 的时间会犯下相同的错误。这种演算法单一栽培证明了，虽然平均决策品质提高，系统性尾部风险却同时升级。

更新后的 Universal Closure 框架建构于 Deng–Hani–Ma 证明架构之上，将局部 closure quality（Q_C）与系统性 closure concentration（H_C）分离。在数学上，当代理数量（N）趋近于无限大时，平均误差的变异数收敛至 σ²ρ，这证明了如果 ρ > 0，增加模型数量无法消除共同误差。因此，表示的多样性必须使用源自误差相关矩阵特征值的有效独立误差数量（N_eff^error）来计算，在完全共线的情况下，该数值会下降至大约 1。

文明不稳定的临界阈值发生在共同模态繁殖数处于临界状态（R_C ≥ 1）且修复与传播的时间比率大于一（χ > 1）之时。为了防止认知边界崩塌与市场中相关的去杠杆化，治理必须强制进行严格的误差共变异数测试，而不是仅仅计算供应商名称的数量。至关重要的是，不可逆行动的自主执行速度必须保持慢于系统性修正频宽（τ_decision < τ_repair），以确保高局部 closure 能够借由全球异质性与快速外部修正来取得平衡。

Artificial intelligence is shifting civilization risks from individual decision errors to common-mode failures caused by shared representations. Although AI decreases individual variance, it increases cross-agent error correlation (ρ). A 2025 ICML study analyzing over 350 large language models revealed that when two models fail on a leaderboard, they make the identical error approximately 60% of the time. This algorithmic monoculture demonstrates that while average decision quality improves, systemic tail risks simultaneously escalate.

The updated Universal Closure framework builds upon the Deng–Hani–Ma proof structure, separating local closure quality (Q_C) from systemic closure concentration (H_C). Mathematically, as the number of agents (N) approaches infinity, the variance of average error converges to σ²ρ, proving that increasing model quantity cannot eliminate shared errors if ρ > 0. Consequently, representational diversity must be calculated using the effective number of independent errors (N_eff^error) derived from the error-correlation matrix eigenvalues, which drops to approximately 1 under complete collinearity.

The critical threshold for civilization instability occurs when the common-mode reproduction number is critical (R_C ≥ 1) and the time ratio of repair to propagation exceeds one (χ > 1). To prevent epistemic boundary collapse and correlated deleveraging in markets, governance must mandate strict error covariance testing rather than merely counting vendor names. Crucially, the autonomous execution speed for irreversible actions must remain slower than the systemic correction bandwidth (τ_decision < τ_repair), ensuring that high local closure is balanced by global heterogeneity and rapid external correction.