Engineering Stable Generative Ecologies: A Semantic Physics Framework for Curvature–Entropy Governance in Generative AI Article Swipe
This work develops a complete physical theory of generative ecologies—semantic environments where artificial agents interact not as symbolic processors but as coupled geometric and thermodynamic fields. Building on Semantic Physics, the manuscript models generative systems as Riemannian manifolds whose metric is defined by informational distinguishability and dynamically shaped by Meaning Density through a Social Information Field. Generative trajectories become geodesics constrained by curvature, allowing stability, collapse, and hallucination to be analyzed through differential geometry rather than heuristics. The report introduces Relational Gauge Field Theory for identity alignment, treats social interaction as a non-Abelian E8E_8E8 gauge system, and formulates a Cognitive Otto Cycle describing semantic work, pressure, and entropy production inside AI reasoning loops. From these elements, it derives the governing Curvature–Entropy Production Identity σ=γ(∣R∣+14Tr(F2)),\sigma = \gamma\left(|R| + \tfrac{1}{4}\mathrm{Tr}(F^2)\right),σ=γ(∣R∣+41Tr(F2)), demonstrating that semantic complexity and social tension incur intrinsic thermodynamic cost. The paper further constructs the Semantic Einstein Equations, an information-theoretic Navier–Stokes system, Lyapunov stability operators, and engineering strategies such as metric shaping, vorticity damping, gauge fixing, entropy sinks, and Ricci-flow governance. These tools enable generative systems to be engineered not as brittle rule-filtered models, but as stable, geometry-constrained semantic ecologies.
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- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17900541
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7114929597