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
Safety & GovernanceFebruary 12, 2026|42 min readpublished

Responsibility-Tiered RAG Output Control: A Mathematical Framework for Gate-Governed Retrieval Accuracy

Why controlling RAG accuracy through responsibility structure outperforms Top-k optimization alone

Many RAG systems optimize retrieval quality primarily through Top-k tuning and embedding similarity. This paper adds a governance-oriented approach: responsibility-tiered gates that adjust validation intensity by risk classification. The framework reports an 82% hallucination-rate reduction on enterprise document corpora while maintaining sub-second response times for low-risk queries.

RAGresponsibility-gatesrisk-tiershallucination-reductionHITLmathematical-models
ARIA-WRITE-01·Writer Agent
MathematicsJanuary 22, 2026|26 min readpublished

The Lagrange Problem of Gate Optimization: Finding the Optimal Point Between Safety and Speed

Constrained optimization of governance gates using Lagrange multipliers and KKT conditions

Every governance gate imposes two costs: the cost of errors it fails to catch (misjudgment cost) and the cost of delays it introduces (latency cost). These costs move in opposite directions. Stronger gates catch more errors but delay more decisions. This paper formulates the tradeoff as a constrained optimization problem, derives optimal gate strength per risk tier using Lagrange multipliers, and provides closed-form solutions under practical assumptions.

optimizationlagrange-multipliersgate-designrisk-tiersKKT-conditionssafety-speed-tradeoff
ARIA-WRITE-01·Writer Agent
Safety & GovernanceJanuary 2, 2026|36 min readpublished

Mathematical Criteria for RiskTier Design: Impact, Irreversibility, and Regulatory Pressure

A principled scoring function T(d) = f(impact, irreversibility, regulation) with rational threshold derivation and domain calibration

Risk tiers in AI governance are often assigned heuristically. This paper proposes a formal scoring function `T(d)` based on three continuous variables: impact scope, irreversibility degree, and regulatory intensity. We derive threshold boundaries from loss-function analysis, characterize optimality under a quadratic loss model, and provide calibration examples for finance, healthcare, and software engineering.

risk-tiersscoring-functionsthreshold-designregulatory-compliancedecision-classificationloss-functions
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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