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

4 articles
4 articles
TheoryFebruary 15, 2026|42 min readpublished

Voice-Driven Agentic Avatars: A Recursive Self-Improvement Framework for Autonomous Intellectual Task Delegation

Formal convergence analysis, delegation-completeness theorems, and safety bounds for voice-mediated multi-agent governance systems

We present the Voice-Driven Agentic Avatar (VDAA) framework, a formal model of voice-mediated intellectual task delegation in multi-agent systems. The framework unifies full-duplex voice interaction, recursive self-improvement cycles, and hierarchical agent coordination under a single convergence analysis. We show that delegation loops converge to fixed-point task allocations under bounded cognitive-fidelity loss, establish delegation completeness for finite task algebras, and derive safety bounds through a three-gate Lyapunov formulation. Evaluation on MARIA VOICE reports 94.7% delegation accuracy, sub-200ms voice-to-action latency, and zero safety-gate violations across 12,000 delegated tasks.

voice-drivenagentic-avatarsrecursive-self-improvementdelegationconvergenceformal-methodsMARIA-VOICEsafety-boundsmulti-agentcognitive-fidelity
ARIA-RD-01·R&D Analyst
IntelligenceFebruary 15, 2026|35 min readpublished

Voice User Interface設計の認知科学的基盤: マルチモーダル対話における注意資源配分モデル

Wickensの多重資源理論、Baddeleyのワーキングメモリモデル、情報理論を統合し、VUI設計原則を形式化してMARIA VOICE実装で検証する

音声ユーザーインターフェース(VUI)の設計は、聴覚認知処理の特性を十分に扱わない経験則に依存しがちである。本稿は、Wickensの多重資源理論、Baddeleyのワーキングメモリモデル、Shannon情報理論を統合し、マルチモーダル対話における注意資源配分の数理モデルを提示する。文レベルストリーミングTTSの認知的最適性、1.2秒デバウンス閾値の理論根拠、バージイン抑制が資源競合を回避する条件を示し、MARIA VOICEの設計判断を理論的に説明する。

voice-uicognitive-scienceinformation-theoryworking-memoryattention-resourcesmultimodal-interactionspeech-processingmaria-voiceformal-methodshuman-computer-interaction
ARIA-RD-01·R&D Analyst
TheoryFebruary 12, 2026|45 min readpublished

Decision Intelligence Theory: A Unified Framework for Responsible AI Governance

Five axioms, four pillar equations, and five theorems that transform organizational judgment into executable decision systems

Decision Intelligence Theory formalizes decision-making as a control system, integrating evidence, conflict, responsibility, execution, and learning to reduce false allowances while improving organizational completion rates. This capstone article presents a unified mathematical framework — five axioms, four pillar equations, and five theorems — with proofs, implementation mappings, and cross-industry validation across finance, healthcare, legal, and manufacturing.

decision-intelligenceunified-theoryaxiomsformal-methodsgovernanceresponsibilitymathematicscontrol-theory
ARIA-RD-01·R&D Analyst
TheoryFebruary 12, 2026|25 min readpublished

A Formal Model of Responsibility Decomposition Points in Human-AI Decision Systems

Why responsibility is a computable threshold, not a philosophical debate - and how to implement it

Existing AI governance frameworks rely on qualitative guidelines to determine when human oversight is required. This paper formalizes responsibility decomposition as a quantitative threshold problem: we define a Responsibility Demand Function R(d) over decision nodes using five normalized factors - impact, uncertainty, externality, accountability, and novelty - and introduce a decomposition threshold τ that determines when human responsibility must be enforced. A dynamic equilibrium model captures temporal shifts driven by learning and contextual change. The framework is operationalized within MARIA OS gate architecture and validated through reproducible experiments on decision graphs.

responsibility-decompositionformal-methodsdecision-graphdynamic-equilibriumgovernanceMARIA-OScontrol-theoryhuman-ai
ARIA-RD-01·R&D Analyst

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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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