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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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CESI Judgment Governance v1.0 - Attention, Irreversibility, and the Future of Human Judgment in the AI Era

Authors: Xu, Lucas Xiaochun;

CESI Judgment Governance v1.0 - Attention, Irreversibility, and the Future of Human Judgment in the AI Era

Abstract

Abstract CESI Judgment Governance v1.0 presents a unified theoretical framework for understanding the erosion, misgovernance, and possible reconstruction of human judgment under AI-accelerated systems. Departing from dominant AI governance paradigms centered on ethics, compliance, and technical control, this monograph reframes governance as a judgment architecture problem, governed by attention allocation, irreversibility, and authority over decision loops. Grounded in the Co-Evolutionary Science of Intelligence (CESI), the work introduces Irreversibility-First Risk Governance (IFRG) and the Irreversibility-Weighted Judgment Loop (IWJL) as core analytical instruments. Across three integrated papers, the monograph demonstrates:(1) how judgment is structurally consumed through attention misallocation,(2) why mainstream AI governance systematically solves the wrong problem, and(3) how judgment sovereignty can be structurally designed and protected. This work contributes a civilization-scale interpretation of AI governance failure and proposes a principled foundation for judgment-preserving systems in the AI era. 摘要 《CESI Judgment Governance v1.0》是一部系统性研究 AI 加速时代人类判断权如何被消耗、被误治理、以及如何被重新设计与保护 的理论专著。 本书明确拒绝将 AI 治理理解为伦理、合规或技术控制问题, 而是将其重新定义为一个判断架构问题: - 谁拥有注意力? - 谁承担不可逆后果? - 谁控制判断回路? 在 CESI (智性共生进化科学) 框架下, 本文以 不可逆性优先风险治理 (IFRG) 与 不可逆加权判断回路 (IWJL) 为核心分析工具, 通过三篇逻辑闭合的论文,系统揭示: 判断如何在看似理性、严谨的决策活动中被结构性消耗 为什么主流 AI 治理在解决“假问题”, 却加速判断退化 判断主权如何通过设计原则被重新夺回 本书提供了一种面向个体、组织与制度层面的 判断优先于流程 的 AI 治理新范式。

Related Works This volume, CESI Judgment Governance v1.0, serves as the theoretical synthesis and structural integration of the CESI Judgment Governance Series. It builds upon and unifies the following previously published works: • Paper I: Xu, L. X. (2026). "无价值纠结" 的量化识别与注意力治理: A CESI–IFRG–IWJL Attention Governance Framework (v1.0). Zenodo. https://doi.org/10.5281/zenodo.18458961 • Paper II: Xu, L. X. (2026). Why Most AI Governance Solves the Wrong Problem — 基于 CESI–IFRG–IWJL 的判断权错位分析 (v1.0). Zenodo. https://doi.org/10.5281/zenodo.18470117 • Paper III: Xu, L. X. (2026). Judgment Governance Design Principles — 在 AI 时代重建不可逆判断主权的结构性原则 (v1.0). Zenodo. https://doi.org/10.5281/zenodo.18470566 While each paper addresses a distinct layer of judgment governance, this volume consolidates their shared theoretical spine—CESI, IFRG, and IWJL—into a coherent judgment governance framework.

Declaration (声明) 本专著为作者在 CESI (Co-Evolutionary Science of Intelligence) 理论体系下的原创研究成果。 文中所有概念、模型与分析仅用于学术研究、判断科学探索及治理结构设计讨论, 不构成法律、监管、合规或投资建议。作者对文中所有判断、推论与结论承担全部责任。

Contribution Statement (学术贡献声明) 本专著的核心贡献不在于提出新的治理工具, 而在于: 重构 AI 风险与治理的问题定义 将判断权从伦理话语转化为结构变量 提供一个判断优先于流程的 AI 治理分析范式 为判断科学、组织设计与制度研究提供可迁移模型

Version Note v1.0:首次将 CESI Judgment Governance 系列整合为统一专著, 形成完整的判断治理三段论。

Keywords

CESI, Irreversibility-First Risk Governance (IFRG), AI Governance Failure, Judgment Sovereignty, Judgment Governance, Irreversibility-Weighted Judgment Loop (IWJL), Attention Governance, Social Physics of Intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
Average
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