TAG ARCHIVE
formal-methods
4 MARIA OS blog articles tagged formal-methods, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
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 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。
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. This capstone article presents a unified mathematical framework — five axioms, four pillar equations, and five theorems — together with implementation mappings and internal cohort analyses across finance, healthcare, legal, and manufacturing.
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.