模型越大越强？新定律揭示机构适应度存在最优规模。
The Institutional Scaling Law: Non-Monotonic Fitness, Capability-Trust Divergence, and Symbiogenetic Scaling in Generative AI
- 提出制度尺度律，模型规模与能力、信任等指标非单调相关
- 发现超大规模时能力与信任会分离，出现能力-信任背离现象
- 适合关注AI治理、系统架构与主权模型的研究者阅读
传统尺度律认为模型性能随规模单调提升。本文提出制度尺度律，表明机构适应度（综合衡量能力、信任、可负担性与主权）在模型规模上呈非单调变化，存在依赖环境的最优规模N*(epsilon)。该框架将韩等（2025）的可持续性指数从硬件层扩展至生态层，证明在临界规模后能力与信任正式分离（能力-信任背离）。进一步推导出共生演化修正项，显示特定领域模型协同系统在本地部署环境中可超越前沿通用模型。研究构建了生成式AI五阶段演化谱系（1943年至今），分析前沿实验室动态、主权AI兴起及后训练对齐演进（从RLHF到GRPO）。制度尺度律预测：下一阶段跃迁将不来自更大模型，而是更优协调的领域专用模型系统。
原文摘要 · Abstract (English)
Classical scaling laws model AI performance as monotonically improving with model size. We challenge this assumption by deriving the Institutional Scaling Law, showing that institutional fitness -- jointly measuring capability, trust, affordability, and sovereignty -- is non-monotonic in model scale, with an environment-dependent optimum N*(epsilon). Our framework extends the Sustainability Index of Han et al. (2025) from hardware-level to ecosystem-level analysis, proving that capability and trust formally diverge beyond critical scale (Capability-Trust Divergence). We further derive a Symbiogenetic Scaling correction demonstrating that orchestrated systems of domain-specific models can outperform frontier generalists in their native deployment environments. These results are contextualized within a formal evolutionary taxonomy of generative AI spanning five eras (1943-present), with analysis of frontier lab dynamics, sovereign AI emergence, and post-training alignment evolution from RLHF through GRPO. The Institutional Scaling Law predicts that the next phase transition will be driven not by larger models but by better-orchestrated systems of domain-specific models adapted to specific institutional niches.
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