DOI: 10.5281/zenodo.18224217
title: TASI-X: A Foundational Architecture for the Long-Term Governance of Evolving Intelligent Systems

description: 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