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The study aims to develop a structured framework for self-awareness in multi-model AI systems to improve their operational governance.

March 14, 2026Open Access

Functional Self-Awareness Architecture for Regulated Autonomous Multi-Model AI Systems

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The study aims to develop a structured framework for self-awareness in multi-model AI systems to improve their operational governance.
Introduced a Functional Self-Awareness Architecture with four integral components: self-model, uncertainty-awareness mechanisms, meta-cognitive evaluation, and self-governance interfaces.
Conducted structured failure-mode analyses on language-model pipelines to evaluate the framework's effectiveness.
Derived minimal architectural conditions necessary for stable reflexive regulation.
Demonstrated that removing any of the derived conditions leads to specific governance vulnerabilities.
Showed that the architecture can effectively regulate action authority and enhance reliability in AI operations.

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This framework introduces a self-awareness architecture to regulate autonomous AI systems, enhancing performance and reliability.

Raghurami Etukuru (2026) studied this question.

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