ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
A rigorous optimal control framework for governing human-AI co-evolution under multi-objective cost functions, partial observability, and hard safety constraints
We reformulate human-AI co-evolution as a constrained optimal-control problem. By defining a multi-objective cost function over task quality, human capability preservation, trust stability, and risk suppression, and solving Bellman-style recursions under hard constraints, we characterize co-evolution policies that Meta Cognition can approximate in MARIA OS. We extend the framework to POMDP settings for partial observability of human cognitive states and derive conditions linked to long-run social stability.
Multi-objective optimization, divergent national AI strategies, and stochastic democratic override dynamics in autonomous governance
Each nation in the Civilization simulation operates a LOGOS AI system that optimizes a five-component sustainability objective: Stability, Productivity, Recovery, Power Dispersion, and Responsibility Alignment. We formalize this as a constrained multi-objective optimization problem, analyze how nations diverge by navigating different regions of the Pareto frontier, and model constitutional amendments as stochastic threshold events that can override AI recommendations. We then characterize conditions under which AI rulings conflict with democratic outcomes.
Formulating the multi-agent decision pipeline as a continuous-time control problem and deriving the optimal governance law
A Decision OS can be modeled as a control system that observes governance state, applies gate/evidence controls, and steers operations toward target conditions. This paper formulates the decision pipeline as a state-space control problem with state vector `x = [risk, compliance, evidence, velocity]`, control `u = [gate_strength, human_review_rate, evidence_threshold]`, and multi-objective cost `J = integral(risk + lambda * delay)dt`. We derive a control law via Pontryagin's maximum principle and characterize co-state dynamics, where optimal gate strength varies with accumulated risk and compliance margin.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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