ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Governance density as organizational self-awareness, a spectral stability condition, and the mathematical foundations of enterprise metacognition
We formalize an agentic company as a graph-augmented constrained Markov decision process G_t = (A_t, E_t, S_t, Pi_t, R_t, D_t) and define operational governance density over router-generated Top-K candidate actions, making D_t directly measurable from logs at each step. We derive a practical stability condition on the damped influence matrix W_eff,t = (1 - kappa(D_t)) W_t, yielding (1 - kappa(D_t)) lambda_max(W_t) < 1. We then show that governance constraints act as organizational metacognition: each constraint is a point where the system observes its own behavior. This frames metacognition not as overhead, but as the control parameter that determines whether an agentic company self-organizes stably or diverges. Planet-100 simulations validate that stable role specialization emerges in the intermediate governance regime.
Dual-model anomaly detection, threshold engineering, gate integration, and real-time stability monitoring for autonomous agent systems
The Doctor system in MARIA OS implements organizational metacognition through dual-model anomaly detection, combining Isolation Forest for structural outlier detection and an Autoencoder for continuous deviation measurement. We detail the combined score A_combined = alpha * s(x) + (1 - alpha) * sigma(epsilon(x)), threshold design (soft throttle at 0.85, hard freeze at 0.92), and Gate Engine integration for dynamic governance-density control. We also define a stability guard that monitors lambda_max(A_t) < 1 - D_t in real time, where A_t is the operational influence matrix. Operational results show F1 = 0.94, mean detection latency of 2.3 decision cycles, and 99.7% prevention of cascading failures.
A formal dynamical-systems treatment of human-AI interaction stability and how metacognitive control helps reduce capability decay and trust instability
We model the human-AI interaction loop as a coupled dynamical system `X_t = (H_t, A_t)` and analyze stability under metacognition-mediated control through spectral-radius conditions on the coupled Jacobian. Simulations across 1,000 trajectories report 94.2% trust-band stability and 87.6% capability preservation versus uncontrolled baselines.
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.
Extending dyadic human-AI co-evolution to societal-scale network dynamics with trust propagation, dependency contagion, phase transitions, and distributed social metacognition
Individual human-AI pair models miss emergent dynamics that appear when many agents interact on complex networks. This paper develops a societal co-evolution framework for trust cascades, dependency contagion, capability hollowing, and phase transitions in AI-augmented organizations, and introduces Social Metacognition as a distributed stabilization mechanism.
From Enterprise Governance to AI Constitutions: How Institutional Economics and Meta-Governance Theory Stabilize Multi-Agent Societies
Multi-agent AI societies require more than individual metacognition: they also require institutional design. This article formalizes agentic-company governance, derives social objective functions for AI-human ecosystems, establishes the Speed Alignment Principle as a stability condition, and presents an AI-constitution model with revision rules. In simulations across 600 runs, adaptive institutional frameworks reduced spectral radius from 1.14 to 0.82 while maintaining audit scores above 0.85.
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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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