RSMO Benchmark v0.1: Radial Synthesis Metacognitive Operator Cross-Model Validation Dataset

This dataset contains the complete experimental protocol, prompts, and results for validating the Radial Synthesis Metacognitive Operator (RSMO) v2.0, a meta-prompting protocol designed to enhance structural reasoning in Large Language Models (LLMs) without model retraining. Key Findings: * Performance: 100% of the evaluated models (GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash, and Grok-2) improved across multiple structural metrics. * Coherence: Achieved an average improvement of 32% in structural coherence, with a Coherence Relacional Network (CRN) metric of 0.866 (vs. 0.50 baseline). * Statistical Significance: The experiment demonstrated a very large effect size (Cohen's d = 2.87) with a significance level of p < 0.001. +4 Applications Demonstrated: 1. Defense Technology: The PHOENIX C-UAS system, incorporating Geneva Convention constraints and offering an 80-90% cost reduction compared to imported alternatives. 2. Healthcare: Precision diagnosis for Complex Regional Pain Syndrome (CRPS) Phase III, showing a potential 40% reduction in failed clinical interventions. +4 This dataset supports reproducible research in AI prompt engineering and demonstrates sovereignty-oriented technology applications for Mexico. +1