What does this research mean for the field?

Systematic event recording and institutional constraints within a cybernetic governance architecture significantly improve AI agent reproducibility, increasing recovery rates by 35 percentage points and reducing fault rates by 62.7%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to explore how cybernetic governance enhances the reproducibility of multi-model AI systems.

April 13, 2026Open Access

Governing AI by Evidence, Not Faith: A Cybernetic Architecture for Auditable Multi-Model Orchestration

Key Points

The study aims to explore how cybernetic governance enhances the reproducibility of multi-model AI systems.
Introduced the Model of Cybernetic Knowledge Architecture (MoCKA) for AI governance.
Implemented systematic event recording practices.
Analyzed the effects of institutional constraints on AI agent performance.
Achieved a total improvement in the ZYXTS lever experiment by +0.6367.
Improved recovery rate by 35 percentage points.
Reduced fault rate by 62.7%.

Abstract

MoCKA (Model of Cybernetic Knowledge Architecture) is a cybernetic governance architecture for multi-model AI orchestration. This preprint presents empirical evidence that systematic event recording and institutional constraints improve AI agent reproducibility. Key results: ZYXTS lever experiment Δtotal=+0.6367, Recovery Rate improvement +35pp, fault rate reduction −62.7%. Target: AIES 2026 / AIGOV@AAAI 2027.

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Cite This Study

masahito kimura (Sat,) studied this question.

synapsesocial.com/papers/69dc89823afacbeac03eb282 https://doi.org/https://doi.org/10.5281/zenodo.19503666

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