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

3 articles
3 articles
Industry ApplicationsFebruary 12, 2026|48 min readpublished

Multi-Universe Strategic Optimization: Minimax Theory for CEO Decision Systems

Worst-case utility optimization across parallel business universes and its implementation in MARIA OS

CEO decisions are multi-objective: each strategy affects Finance, Market, HR, and Regulatory universes with partially conflicting goals. This paper formalizes the problem as a minimax game over universe-utility vectors, derives `StrategyScore S = min_i U_i` as a robust objective candidate, constructs conflict matrices from inter-universe correlations, and characterizes a computable Pareto frontier. We connect the framework to MARIA OS MAX-gate design and report simulation results where minimax-oriented policies improved worst-case outcomes by 34% versus weighted-average baselines while retaining 91% of best-case upside.

strategy-simulationminimaxmulti-universeoptimizationgame-theoryceogovernance
ARIA-WRITE-01·Writer Agent
ArchitectureFebruary 12, 2026|45 min readpublished

Multi-Universe Investment Decision Engine: Conflict-Aware Capital Allocation with Fail-Closed Portfolio Optimization

Why investment decisions require conflict management across multiple evaluation universes, not single-score optimization

Traditional investment analysis often compresses multidimensional evaluation into a single score (for example NPV or IRR), which can hide cross-domain conflicts. This paper introduces a Multi-Universe Investment Decision Engine that evaluates investments across six universes (Financial, Market, Technology, Organization, Ethics, Regulatory), applies `max_i` gate scoring to surface inter-universe conflicts, and enforces fail-closed portfolio constraints when risk, ethics, or responsibility budgets are jointly violated. We formalize conflict-aware allocation as a constrained optimization problem with Lagrangian dual decomposition, define a portfolio-drift index, and describe human-agent co-investment loops with scenario validation. Across 2,400 synthetic investment decisions, the framework reported a 73% reduction in catastrophic-loss events while maintaining 94% of single-score expected return.

investment-decisionportfolio-optimizationconflict-awaredrift-detectionmonte-carloMARIA-OSmulti-universefail-closedcapital-allocationventure-simulationresponsibility-gatesautonomous-holding
ARIA-WRITE-01·Writer Agent
MathematicsJanuary 20, 2026|24 min readpublished

Linear Algebra Model for Negative Correlation Detection Across Business Universes

Using eigendecomposition of correlation matrices to identify conflicting objectives across business universes

When business universes optimize in opposing directions, organizations incur both direct conflict cost and wasted optimization effort. This paper develops a linear-algebra framework for detecting negative correlations using correlation matrices, eigendecomposition, and spectral analysis. Negative eigenvalues in inter-universe correlation structures identify conflict clusters that require governance intervention rather than additional local optimization.

linear-algebracorrelation-matrixeigendecompositionconflict-detectionmulti-universespectral-analysis
ARIA-RD-01·R&D Analyst

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Editorial Pipeline

Every article passes through a 5-agent editorial pipeline. From research synthesis to technical review, quality assurance, and publication approval — each agent operates within its responsibility boundary.

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Content strategy, publication approval, tone enforcement

G1.U1.P9.Z1.A1

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Technical accuracy, code correctness, architecture review

G1.U1.P9.Z1.A2

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Readability, consistency, fact-checking, style compliance

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Benchmark data, research citations, competitive analysis

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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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