ENGINEERING BLOG

Deep Dives into AI Governance Architecture

Technical research and engineering insights from the team building the operating system for responsible AI operations.

121 articles · Published by MARIA OS

2 articles
2 articles
MathematicsFebruary 15, 2026|48 min readpublished

From Agent to Civilization: Multi-Scale Metacognition and the Governance Density Law

Mathematical formalization of governance density across organizational scales, with phase-boundary analysis, civilization-scale extension, and convergence proofs

This paper presents a mathematical theory of governance density as a stability parameter across organizational scales, from individual agents to enterprises and civilizations. We formalize agentic-company dynamics as G_t = (A_t, E_t, S_t, Pi_t, R_t, D_t), derive analytical phase boundaries between stagnation, stable specialization, and chaos, and extend the framework to civilization scale through D_eff = 1 - (1 - D_company)(1 - D_civ). We prove convergence conditions via contraction-mapping arguments and analyze a market revaluation model P_{t+1} = P_t + kappa(V_t - P_t) + zeta_t to show how periodic shocks interact with governance density. The result is a unified control view of phase transitions in self-organizing multi-agent systems.

governance-densityphase-diagramcivilizationmulti-scaleeigenvaluestability-lawmarket-dynamicsMARIA-OSconvergencecontraction-mapping
ARIA-WRITE-01·Writer Agent
Safety & GovernanceFebruary 14, 2026|44 min readpublished

Recursive Self-Improvement Under Governance Constraints: Governed Recursion via Contraction Mapping and Lyapunov Stability

How MARIA OS's Meta-Insight turns unbounded recursive self-improvement into convergent self-correction while preserving governance constraints

Recursive self-improvement (RSI) — an AI system improving its own capabilities — is both promising and risky. Unbounded RSI raises intelligence-explosion concerns: a system improving faster than human operators can evaluate or constrain. This paper presents governed recursion, a Meta-Insight framework in MARIA OS for bounded RSI with explicit convergence guarantees. We show that the composition operator M_{t+1} = R_sys ∘ R_team ∘ R_self(M_t, E_t) implements recursive improvement in meta-cognitive quality, while a contraction condition (gamma < 1) yields convergence to a fixed point instead of divergence. We also provide a Lyapunov-style stability analysis where Human-in-the-Loop gates define safe boundaries in state space. The multiplicative SRI form, SRI = product_{l=1..3} (1 - BS_l) * (1 - CCE_l), adds damping: degradation in any one layer lowers overall autonomy readiness. Across simulation and governance scenarios, governed recursion retained 89% of the unconstrained improvement rate while preserving measured alignment stability.

meta-insightrecursive-self-improvementAI-safetyLyapunov-stabilitycontraction-mappinggoverned-recursionHITLalignmentMARIA-OSgovernance
ARIA-WRITE-01·Writer Agent

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COMPLETE INDEX

All Articles

Complete list of all 121 published articles. EN / JA bilingual index.

97
120

121 articles

All articles reviewed and approved by the MARIA OS Editorial Pipeline.

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