TheoryFebruary 22, 202648 min read

Agentic Ethics Lab: Designing a Corporate Research Institute for Structural Ethics in AI Governance

A four-division, gate-governed research architecture that transforms ethics from philosophical declaration into executable, auditable, and evolvable system infrastructure

Ethics declarations without structural enforcement are organizational theater. This paper presents the Agentic Ethics Lab — a corporate research institute embedded within the MARIA OS governance architecture, operating as a first-class Universe with four specialized divisions: Ethics Formalization, Ethical Learning, Agentic Company Design, and Governance & Adoption. Each division runs agent-human hybrid teams under fail-closed research gates. We formalize the lab's architecture using decision graph theory, prove that self-referential governance research preserves safety invariants, and demonstrate that a corporate research institute with no revenue targets but strategic alignment outperforms both pure academic and pure product research in responsible AI advancement.

agentic-ethics-labresearch-architectureethics-formalizationethical-learningagentic-companygovernancefail-closedMARIA-OSdecision-graphresponsible-ai
TheoryFebruary 22, 202648 min read

Agentic Ethics Lab:AIガバナンスにおける構造的倫理のための企業研究所の設計

倫理を哲学的宣言から実行可能・監査可能・進化可能なシステムインフラストラクチャへと変革する、4部門・Gate管理型研究アーキテクチャ

構造的な強制力を伴わない倫理宣言は、組織的な演劇に過ぎない。本論文では、MARIA OSガバナンスアーキテクチャ内に組み込まれた企業研究所である Agentic Ethics Lab を紹介する。この研究所は4つの専門部門(Ethics Formalization、Ethical Learning、Agentic Company Design、Governance & Adoption)を持つファーストクラスのUniverseとして運用される。各部門はFail-Closedの研究Gateの下でAgent-人間ハイブリッドチームを運営する。本論文では、決定グラフ理論を用いてラボのアーキテクチャを形式化し、自己参照的ガバナンス研究が安全性不変量を保持することを証明し、収益目標を持たないが戦略的に整合した企業研究所が、純粋な学術研究や純粋な製品研究の双方よりも責任あるAI推進において優れた成果を上げることを実証する。

agentic-ethics-labresearch-architectureethics-formalizationethical-learningagentic-companygovernancefail-closedMARIA-OSdecision-graphresponsible-ai
TheoryFebruary 12, 202652 min read

Agentic R&D as Governed Decision Science: Six Research Frontiers for Speed, Quality, and Responsibility in Judgment Operating Systems

How to build a self-improving governance OS through six mathematical research programs, four agent teams, and a Research Universe architecture

Judgment is harder to scale than execution, especially in high-stakes decision environments. This paper presents six research frontiers — from hierarchical speculative pipelines to constrained reinforcement learning — for extending MARIA OS from product operations into governed decision science. We formalize each frontier with mathematical models, design four agent-human hybrid research teams, and introduce the Research Universe: a governance structure where each experiment is evaluated through the same fail-closed gates it studies.

agentic-rdresearch-architecturespeculative-pipelineincremental-evaluationbelief-calibrationconflict-quality-loopconstrained-rlhuman-in-the-loopresearch-universejudgment-science