arXiv cs.AI· Kim Phuc Tran·· 12 小时前AI 评分18
谁能改变航向？AI 主权与集体学习能力的整合性综述
Who Can Change Course? An Integrative Review of AI Sovereignty and Collective Learning Capacity
AI 导读
一篇 arXiv 综述以法国为例，将 AI 主权界定为国家的学习能力，并借用 HCLM（熵调控表征学习）框架关联算力、数据、人才等投入与协调失灵、行政重复、能源约束等可避免损失。文章提出条件性缩放关系与政策指标，结合 OECD、IMF 和世界银行描述性证据，并强调该框架是研究启发式而非把神经网络方程直接套用于国家。
正文 · 原文
Abstract:This paper examines AI sovereignty as national learning capacity, using France as an application. Human-Centered Learning Mechanics (HCLM), proposed for entropy-regulated representation learning (Tran, 2026), provides a conceptual lens for relating information inputs, absorptive capacity and avoidable institutional losses. Inputs include compute, data, talent, research, capital, deployment and institutional experimentation. Potential losses include coordination failures, administrative duplication, energy constraints, regulatory uncertainty and limited organizational uptake. Regulation also supports learning through evaluation, trusted data access, interoperability and safety standards. The argument is that sovereignty depends on converting distributed resources and knowledge into usable, coordinated and responsibly evaluated capability. The analysis connects this account with neural scaling laws, endogenous growth, creative destruction and standard game-theoretic mechanisms. It develops conditional scaling relationships, policy indicators and illustrative scenarios, alongside descriptive OECD, IMF and World Bank evidence. Country observations are kept separate from assumed scenario values, and a fully specified public-goods example shows how decentralized incentives can limit shared provision. The framework offers a research heuristic rather than a literal transfer of neural-network equations to countries or an established measure of sovereignty. Its empirical value requires testing with longitudinal, organizational and cross-country data.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.00729 [cs.AI] |
| (or arXiv:2606.00729v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.00729 arXiv-issued DOI via DataCite |
Submission history
From: Kim Phuc Tran [view email]
[v1]
Sat, 30 May 2026 13:49:42 UTC (512 KB)
[v2]
Wed, 17 Jun 2026 12:19:59 UTC (515 KB)
[v3]
Fri, 16 Oct 2026 12:05:13 UTC (33 KB)
来源：arXiv cs.AI · arxiv.org