TASI-X: A Foundational Architecture for the Long-Term Governance of Evolving Intelligent Systems
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TASI-X: A Foundational Architecture for the Long-Term Governance of Evolving Intelligent Systems Final Complete Ultimate Edition (2026) AuthorDr. B. MazumdarIndependent Researcher–Scholar(AI Governance · Cybersecurity · Post-Quantum Cryptography · Digital Statecraft)ORCID: 0009-0007-5615-3558DOI: https://doi.org/10.5281/zenodo.18224218 Abstract TASI-X (Transcendent Autonomous Systems Intelligence) introduces a foundational architectural framework for the long-term, civilizational-scale governance of advanced and evolving intelligent systems. Departing fundamentally from prevailing regulatory and safety paradigms, this work reframes high-impact AI not as a bounded technical artifact, regulated product, or compliance object, but as an emergent socio-technical institution whose authority, legitimacy, and influence compound over time. Within this reframing, conventional mechanisms—rules, audits, behavioral controls, capability thresholds, and post-hoc oversight—are shown to be structurally insufficient for governing systems that evolve, self-modify, integrate into institutions, and accumulate power across decades. TASI-X responds to this gap by advancing architectural governance as the primary and enduring mechanism for control, legitimacy, and stability. The framework offers a rigorous, non-speculative design science addressing governance challenges that intensify over long temporal horizons, including evolutionary drift, value erosion, authority capture, institutional dependency, and alignment decay. Rather than attempting to optimize intelligence or constrain behavior ex post, TASI-X embeds permanent governability, structural self-constraint, and constitutional limits directly into system architecture. Core Contributions This work makes the following original and foundational contributions to AI governance and long-term safety research: Establishes architectural governance as the primary mechanism for durable AI safety, legitimacy, and institutional control Formalizes the Ontological Constraint Layer, defining permanently non-delegable human, constitutional, and civilizational authorities Introduces the Evolutionary Governance Engine, designed to supervise, constrain, and audit system trajectories across extended temporal horizons Elevates Meta-Value Preservation from an ethical aspiration to a non-negotiable structural design requirement Reframes alignment as permanent governability, rather than optimization, preference learning, or static safety guarantees Provides a neutrality-preserving architecture compatible with pluralistic legal, cultural, political, and constitutional systems Collectively, these contributions position TASI-X not as a policy proposal, technical safety patch, or near-term regulatory intervention, but as a civilizational-scale governance architecture intended for decades-long relevance. Policy and Institutional Relevance Appendix A — Strategic Policy Architecture of the TASI-X Framework translates the core architectural principles into actionable, internationally compatible policy guidance without imposing ideological, cultural, or political value systems. The framework is directly applicable to: National governments and sovereign regulatory authorities Multilateral institutions (including the United Nations, OECD, G20, and affiliated bodies) Advanced AI research laboratories and frontier model developers Defence, strategic stability, and national security communities Standards bodies and long-horizon governance initiatives The policy architecture emphasizes constitutional safeguards, evolution-aware oversight, governability-by-design standards, and multilateral coordination mechanisms that preserve sovereignty while enabling global stability. Scope and Positioning This work is explicitly: Conceptual and architectural, not empirical or speculative Technology-agnostic and future-proof by design Legally, constitutionally, and institutionally compatible Non-ideological, analytically neutral, and governance-focused Intended for decades-scale relevance rather than short policy cycles It is designed to function as a foundational reference for future research, governance frameworks, institutional design, and international coordination concerning advanced and evolving intelligent systems. Intended Audience AI governance and long-term safety researchers Public policy, international relations, and strategic studies scholars Constitutional, administrative, and technology law experts Systems architects and institutional design researchers Government, defence, and multilateral decision-makers Citation Recommendation Mazumdar, B. (2026). TASI-X: A Foundational Architecture for the Long-Term Governance of Evolving Intelligent Systems [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18224218 Keywords Artificial Intelligence GovernanceArchitectural GovernanceLong-Term AI SafetyCivilizational RiskEvolving Intelligent SystemsMeta-Value PreservationGovernability-by-DesignStrategic AI Policy
TASI-X：面向演进智能系统长期治理的基础架构 最终完整版（2026） 作者：B·马祖姆达尔博士 独立研究员/学者（研究领域：人工智能治理、网络安全、后量子密码学、数字治国） ORCID：0009-0007-5615-3558 DOI：https://doi.org/10.5281/zenodo.18224218 摘要 TASI-X（超自主系统智能，Transcendent Autonomous Systems Intelligence）提出了一套面向先进演进智能系统的、具备文明尺度的长期治理基础架构框架。本研究彻底跳出当前主流的监管与安全范式，将高影响力人工智能（AI）不再界定为受边界约束的技术产物、受监管产品或合规对象，而是将其重塑为一种涌现的社会技术制度，其权威、合法性与影响力随时间不断累积。 在这一重塑框架下，传统治理机制——包括规则制定、审计、行为管控、能力阈值设置及事后监管——被证明在结构上无法适配那些历经数十年演进、自我修改、融入社会制度并不断积累权力的智能系统治理需求。TASI-X正是针对这一治理缺口，提出将架构治理（Architectural Governance）作为实现管控、合法性与稳定性的核心且长效的机制。 该框架提供了一套严谨的、非思辨性的设计科学方法，用于应对长期时间尺度下不断加剧的治理挑战，包括演进漂移、价值侵蚀、权威俘获、制度依赖及对齐失效。TASI-X并未试图优化智能性能或事后约束系统行为，而是将永久可治理性、结构自我约束及制度性边界直接嵌入系统架构之中。 核心贡献 本研究在人工智能治理与长期安全研究领域做出以下原创性基础贡献： 1. 确立架构治理（Architectural Governance）作为实现长效AI安全、合法性与制度管控的核心机制 2. 正式提出本体约束层（Ontological Constraint Layer），明确永久不可让渡的人类、制度性与文明层面的权威边界 3. 引入演进治理引擎（Evolutionary Governance Engine），用于在长期时间尺度内监督、约束并审计系统的演进轨迹 4. 将元价值留存从伦理愿景提升为不可妥协的结构性设计要求 5. 将对齐问题重塑为永久可治理性，而非性能优化、偏好学习或静态安全保障 6. 提供具备中立性的架构，可兼容多元法律、文化、政治与制度体系 综上，这些贡献将TASI-X定位为一套具备数十年适用性的文明尺度治理架构，而非一项政策提案、技术安全补丁或短期监管干预措施。 政策与制度相关性 附录A——TASI-X框架的战略政策架构，将核心架构原则转化为可落地、国际兼容的政策指引，且不会强加任何意识形态、文化或政治价值体系。 本框架可直接应用于以下场景： - 各国政府与主权监管机构 - 多边机构（包括联合国、经济合作与发展组织、二十国集团及其附属机构） - 前沿人工智能研究实验室与大模型开发者 - 国防、战略稳定与国家安全领域相关方 - 标准制定机构与长期治理倡议组织 该政策架构强调制度性保障、适配演进的监管、可治理性优先的设计标准，以及在维护主权的同时实现全球稳定的多边协调机制。 范围与定位 本研究明确属于以下范畴： - 概念性与架构性研究，而非实证或思辨性研究 - 技术中立且设计层面具备前瞻性 - 具备法律、制度与架构兼容性 - 非意识形态化、分析中立且聚焦治理 - 旨在适配数十年尺度的应用场景，而非短期政策周期 本框架旨在为先进演进智能系统相关的未来研究、治理框架、制度设计及国际协调提供基础参考。 目标受众 - 人工智能治理与长期安全研究人员 - 公共政策、国际关系与战略研究学者 - 宪法、行政与科技法律专家 - 系统架构师与制度设计研究人员 - 政府、国防与多边机构决策者 引用格式建议 马祖姆达尔B. (2026). TASI-X：面向演进智能系统长期治理的基础架构 [数据集]. Zenodo. https://doi.org/10.5281/zenodo.18224218 关键词 人工智能治理（Artificial Intelligence Governance）、架构治理（Architectural Governance）、长期AI安全（Long-Term AI Safety）、文明尺度风险（Civilizational Risk）、演进智能系统（Evolving Intelligent Systems）、元价值留存（Meta-Value Preservation）、可治理性优先设计（Governability-by-Design）、人工智能战略政策（Strategic AI Policy）
