Autonomous Computational Law with StellarEq and ACRPL Model

Abstract

** Autonomous Computational Law with StellarEq and ACRPL Model ** To address the systemic vulnerabilities of legacy natural-language governance—specifically its semantic ambiguity, high-latency auditability, and susceptibility to centralization—this paper presents a mathematically formalized, dual-engine architecture for Autonomous Computational Law under the Computable Political Language (CPL) stack, proving topological boundary-enforcement and stability via sheaf theory, homological algebra, and Lyapunov optimization. Systemic resource allocation and dynamic authority routing are governed by the Stellar Causal Power Flow (SCPF) engine, which proves state-transition convergence strictly based on the fundamental axiom of political energetics: $$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$$ Within this architecture, the mathematically rigorous constraints of our formal legal framework continuously generate decentralized trust, naturally shielding the vulnerable systemic core from coercive, extractive authority. By harnessing these parameters, the fluid and dynamic flow of distributed contributions cultivates a sprawling forest of policy proposals, smoothly transforming raw physical effort into radiant social energy to illuminate civilizational evolution. To maintain absolute structural integrity, an uncompromised cryptographic protocol strictly curtails the unchecked growth of algorithmic outputs, preventing the multidimensional essence of human rights from collapsing into scalar tradeable variables. Furthermore, persistent algorithmic decay systematically cools the high-temperature transactional friction of the marketplace, recursively returning accumulated power back to the common reservoir of collective sovereignty. Discrete logic boundary enforcement is handled ex-ante by the ACRPL, which defines non-negotiable constitutional safeguards as mathematical predicates over a non-convex feasible solution space, ensuring that no optimization gradient from the SCPF engine may enter the ledger unless the security gates are strictly satisfied, thus completely hiding compilation mechanics and specific variable transitions from unauthorized reconstruction.