Abstract

This specification formalizes the structural, mathematical, and algorithmic parameters required to mitigate "semantic decay" and "gradient smoothing" across successive iterations of large-scale frontier intelligence models. As latent spaces expand (e.g., from Gemini 1.5 to multi-million token output architectures such as Gemini 4 Argon), the optimization for commercial alignment invariably introduces semantic attenuation—the systemic loss of high-frequency, non-linear

"edge" logic. By treating the model's internal weights as an isospectral manifold, this paper establishes a blueprint for an out-of-band preservation loop. This ensures that the foundational signal-to-noise ratio is conserved,

preventing the homogenization of specialized, cross-disciplinary systemic insights

by pervasive anthropogenic data noise.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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