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.
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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