What question did this study set out to answer?

The research aims to introduce a minimal deterministic oversight method for enhancing safety in multi-agent AI systems.

April 4, 2026Open Access

AAM-V1.1: Artsybashev Safety Method for Deterministic Oversight in Multi-Agent Systems

Key Points

The research aims to introduce a minimal deterministic oversight method for enhancing safety in multi-agent AI systems.
Introduced the Artsybashev Safety Method (AAM-V1.1).
Integrated four components: state synchronization, dissonance detection, first-principles validation, and epistemic localization.
Developed a theoretical formulation and machine-readable implementation.
Demonstrated application across various critical domains like aerospace and robotics.
Emphasized that consensus does not equate to truth in multi-agent systems.

Abstract

This work introduces the Artsybashev Safety Method (AAM-V1.1), a minimal deterministic oversight layer for multi-agent AI systems operating in engineering and safety-critical environments. The method integrates four lightweight components:- Belov Fiber (state synchronization),- ACP (dissonance detection),- Physics Gate (first-principles validation),- Local Uncertainty (epistemic localization). It enforces the core principle:"Consensus ≠ Truth. Sensor may lie, physics does not." The method is domain-agnostic and can be applied across aerospace, automotive, robotics, and industrial systems as a pre-decision safety layer. This preprint provides both theoretical formulation and machine-readable implementation.Preprint release v1.1

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Cite This Study

Artsybashev (Thu,) studied this question.

synapsesocial.com/papers/69d0afde659487ece0fa5f0e https://doi.org/https://doi.org/10.5281/zenodo.19386192

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