A contribution to solving the existential anxiety problem of AI hallucinations
The paper introduces Axiom-1, a post-generation framework aimed at reducing hallucinations in large language models. It employs a six-stage filtering process and a continuous 12.8 Hz resonance pulse to ensure output stability. This approach shifts focus from stochastic generation to governed validation, targeting high-stakes applications like medicine and law.
- ▪Axiom-1 is a structural reliability framework designed to eliminate hallucinations in large language models.
- ▪The system uses a six-stage filtering mechanism and a 12.8 Hz resonance pulse to enforce topological stability.
- ▪It represents a shift from stochastic generation to governed validation for improved AI reliability.
- ▪The framework is intended for use in critical domains such as medicine, law, and national economic planning.
- ▪Axiom-1 is part of a broader effort to create sovereign and trustworthy AI systems.
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Published April 16, 2026 | Version v1 Conference paper Open A1M (AXIOM-1 Sovereign Matrix) for Governing Output Reliability in Stochastic Language Models Authors/Creators Mohamed Samir Abdelrahman Selim Description "This paper introduces Axiom-1, a novel post-generation structural reliability framework designed to eliminate hallucinations and logical instability in large language models. By subjecting candidate outputs to a six-stage filtering mechanism and a continuous 12.8 Hz resonance pulse, the system enforces topological stability before output release.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Zenodo.
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