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| Format: | Recurso digital |
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| Veröffentlicht: |
Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18072835 |
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Inhaltsangabe:
- <p>The parallel challenges of confining high-temperature tokamak plasmas and maintaining coherence in Artificial General Intelligence (AGI) have historically been treated as distinct problem sets—one governed by magnetohydrodynamics (MHD) and the other by high-dimensional semantic vector space. This publication demonstrates that they are, in strict control-theoretic terms, isomorphic expressions of the same underlying stability problem: the confinement of high-energy complexity within a bounded topology. We present the Viriato-TCN Bridge, a theoretical framework that maps the "Plasmoid Instability" equations of Nuno Loureiro directly onto the "Hallucination Cascade" of Large Language Models. By applying the Torsion Control Network (TCN) to this isomorphism, we derive a new method for Active Cognitive Damping, lowering the "Semantic Lundquist Number" to prevent narrative fracturing. This protocol offers a first-principles, physics-based architecture for AGI safety.</p>