ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Modeling how organizational learning rate emerges from meta-cognitive feedback loops via dynamical systems theory, with equilibrium analysis, bifurcation boundaries, and control strategies for sustained intelligence growth
Organizational learning rate (OLR) in multi-agent governance platforms is often treated as a tunable setting instead of an emergent system property. This paper models OLR as the outcome of coupled dynamics among knowledge accumulation, bias decay, and calibration refinement across the MARIA coordinate hierarchy. We formalize a three-dimensional system S(t) = (K(t), B(t), C(t)) with coupled ordinary differential equations, where K is collective knowledge stock, B is aggregate bias level, and C is system-wide calibration quality. We derive equilibria, prove a stable attractor under sufficient meta-cognitive feedback, characterize bifurcation boundaries between learning and stagnation, and map a four-region phase portrait in (K, B, C) space. Across 16 MARIA OS deployments (1,204 agents), the model predicts OLR trajectories with R^2 = 0.91 and flags stagnation risk an average of 21 days before onset.
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.
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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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