CNVS, Monte Carlo Test Suite: Executable Validation Report

Distributed systems traditionally rely on Byzantine Fault Tolerance (BFT) and threshold consensus models to secure global state validation. The Closed Native Verification Systems (CNVS) framework proposes an alternative deterministic architecture, securing distributed state transitions through non-uniform geometric fragmentation and hidden invariant binding over finite fields, eliminating the need for peer-to-peer quorum agreement. This Technical Report presents the Monte Carlo Test (MTC) Suite, a comprehensive evaluation methodology designed to stress-test the theoretical bounds and structural properties of the CNVS framework. Comprising twelve progressive evaluations—spanning large-scale stochastic projections, deterministic sizing formulas, and in-silico executable software pipelines—the analysis explores system behavior under dependent collusion dynamics, accelerating min-entropy erosion, and extreme topological exposure. The empirical results demonstrate that, under the modeled assumptions, systemic compromise is mathematically decoupled from the mere physical capture of peripheral verifiers. Executable structural models concretely instantiate the V_L --> Cons_R --> Inv_C --> V_G pipeline, verifying the strict operational separation between local data admissibility, message authentication, and global semantic consistency. Furthermore, conditional cryptoeconomic projections illustrate how structural scaling (critical fragmentation cardinality, m) systematically suppresses the expected attack payoff, enabling geometric deterrence even in mass-collusion scenarios. The suite does not assert unconditional security for deployed networks; rather, it provides rigorous stochastic and executable evidence that geometric redundancy, bounded leakage, and epistemic isolation jointly redefine fault tolerance in consensus-free verification architectures. Licensing Information: This repository operates under a strict dual-licensing model to protect the author's intellectual property while facilitating technical due diligence, cryptographic auditing, and peer-review reproduction: Executable Code (.py, .html/JS): Licensed under the PolyForm Noncommercial License 1.0.0. Commercial use, enterprise deployment, or inclusion in for-profit products is strictly prohibited without prior written authorization from the author. Academic review and technical auditing are explicitly permitted. Documentation and Reports (.md, .pdf): Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).