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March 27, 20260 citationsOpen Access

The Constitutional Hamiltonian: Deriving Observable Consequences from Topological First Principles

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SHStephen Hope

Key Points

  • The aim is to create a Hamiltonian framework for constitutional AI governance based on topological principles.
  • Developed a non-Hermitian Hamiltonian incorporating knot invariants like Jones and Alexander polynomials.
  • Transformed constitutional compliance into a physical energy landscape.
  • Derived observable consequences from mathematical frameworks.
  • Found an exponential coherence time scaling with knot complexity.
  • Identified a universal drift threshold of 0.17.
  • Established a 3.33 ms operational heartbeat in the AI system.
  • Discovered a spectral linewidth-knot correspondence.

Abstract

This document presents a first‑principles Hamiltonian framework for constitutional AI governance. By embedding knot invariants (Jones and Alexander polynomials) directly into a three‑term non‑Hermitian Hamiltonian, it transforms constitutional compliance from a set of behavioral rules into a physical energy landscape. The framework derives observable consequences: an exponential coherence time scaling with knot complexity, a universal drift threshold of 0.17, a 3.33 ms operational heartbeat, and a spectral linewidth‑knot correspondence. It unifies AI safety, strategic economics, and topological physics, providing a mathematically self‑consistent foundation for verifiable, fail‑closed sovereign AI systems.

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Cite This Study

Stephen Hope (2026) studied this question.

synapsesocial.com/papers/69c61ff615a0a509bde18606https://doi.org/10.5281/zenodo.19214146
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