This preprint introduces a new safety method that enhances oversight in multi-agent AI systems, implying improved security in critical environments.
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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Artsybashev (2026) studied this question.
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