CNVS: Monte Carlo Validation Framework for Fragmentation-Based Distributed Verification Architectures

Description This work presents the CNVS (Consensus Neural Validation System) Monte Carlo validation framework, an exploratory theoretical architecture for fragmentation-based distributed verification under probabilistic, entropic, and topological constraints. The study investigates how semantic fragmentation, residual Shannon entropy, bounded physical redundancy, inferential propagation, and graph-dependent informational dynamics influence unauthorized reconstruction probability in distributed verification environments. The framework progressively evaluates: exact physical fragment acquisition dynamics (Coupon Collector processes), informational granularity scaling, redundancy sensitivity, residual entropy preservation, inferential adversarial accumulation, weighted fragment distributions, and topological graph-based inferential propagation. All simulations are implemented through stochastic Monte Carlo methodologies without hardcoded consensus thresholds, allowing reconstruction dynamics to emerge directly from fragment acquisition probability, residual informational incompleteness, and entropy-constrained inference processes. The repository contains: the full theoretical report, Python Monte Carlo simulation source code, graphical simulation outputs, and methodological assumptions used throughout the validation framework. This work should be interpreted as an exploratory computational and information-theoretic research framework rather than a formally proven consensus protocol or production-ready distributed architecture.