<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ERA</journal-id><journal-title-group><journal-title>Engineering Research and Application</journal-title></journal-title-group><issn>2995-3154</issn><eissn>2993-2742</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ERA.2026100031</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>AGI时代人类文明主体性：嵌套能动性框架、数据驱动屏障与可验证边界</title><url>https://artdesignp.com/journal/ERA/4/10/10.61369/ERA.2026100031</url><author>辜茂军</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>10</issue><history><date date-type="pub"><published-time>2026-10-20</published-time></date></history><abstract>前沿大模型已在数学推理、代码生成与科学发现等任务达到人类专家水平，AGI 落地预期持续提前。现有 AI 安全研究以价值对齐为主线，无法回应文明主体性的核心命题：谁有权定义系统目标、终审输出价值、终止运行进程。当三类权能随效率诉求渐进式外包给自主智能体，人类将面临主体性渐进侵蚀风险。本文将抽象的文明主体性转化为可度量的工程化概念，提出能动性三元组核心框架：主体性等价于同时持有目标定义权、价值终审权与进程中断权，任意一项权能归零即主体性丧失。基于鲁棒控制屏障函数与自由能原理，构建嵌套能动性数学模型；针对 AGI 系统高维非凸、动力学不可微的特征，引入数据驱动屏障与统计屏障验证替代纯解析约束；以碳基生命内稳态与社会共情联合构成的生命势函数为约束场，给出代理测量方案与不等式松弛形式。推导中断时延上界与基于变分互信息估计（InfoNCE）的在线审计判据，提出四项可证伪预测与分层验证方案。本研究为 AGI 文明风险治理提供可落地的度量框架。</abstract><keywords>通用人工智能,文明主体性,控制屏障函数,数据驱动安全,互信息估计,AI治理</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]OpenAI.GPT-6 Astra Technical Report[R].OpenAI,2026.[2]Bengio Y,et al.International AI Safety Report 2026[R].International AI Safety Initiative,2026.[3]Amodei D,et al.Concrete problems in AI safety[J].arXiv preprint arXiv:1606.06565,2016.[4]Bostrom N.Superintelligence:Paths,Dangers,Strategies[M].Oxford University Press,2014.[5]Krakovna V,et al.Specification gaming:the flip side of AI ingenuity[J].DeepMind Safety Research,2020.[6]Greenblatt R,et al.Alignment faking in large language models[J].arXiv preprint arXiv:2412.14093,2024.[7]Friston K.The free-energy principle:a unified brain theory?[J].Nature Reviews Neuroscience,2010,11(2):127-138.[8]Craig A D.How do you feel?Interoception:the sense of the physiological condition of the body[J].Nature Reviews Neuroscience,2002,3(8):655-666.[9]Clark A,Chalmers D.The extended mind[J].Analysis,1998,58(1):7-19.[10]Orseau L,Armstrong S.Safely interruptible agents[C]//UAI.2016.[11]Doran D,et al.What is explainable AI?[J].arXiv preprint arXiv:1710.00794,2017.[12]McAllister R,et al.Concrete problems for autonomous agent safety[J].arXiv preprint arXiv:1606.06565,2016.[13]Ames A D,et al.Control barrier functions:Theory and applications[C]//2019 18th European Control Conference.IEEE,2019:3421-3427.[14]Taylor A J,et al.Adaptive safety with control barrier functions[C]//ACC.IEEE,2020.[15]Srinivasan M,et al.Data-driven safety verification using neural barrier certificates[J].IEEE TAC,2025.[16]Ojala T,et al.Scenario-based statistical barrier certificates for black-box systems[J].IEEE TAC,2025.[17]Oord A v d,et al.Representation learning with contrastive predictive coding[J].arXiv preprint arXiv:1807.03748,2018.[18]陈世卿.第三脑:超级智能与人类未来文明[M].中国科学出版社,2024.[19]Anthropic.Claude&amp;rsquo;s constitution[R].Anthropic,2024.[20]Belghazi M I,et al.MINE:mutual information neural estimation[C]//ICML.2018.[21]Russell S.Human Compatible:Artificial Intelligence and the Problem of Control[M].Viking,2019.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
