30. TEORÍA DE LA POTENCIALIDAD CONSCIENTE (TPC): BENCHMARK DE CAPACIDADES COGNITIVAS EN IA - APLICACIÓN DEL PROTOCOLO RFC-EVAL-001 V1.1. RESULTADOS COMPLETOS DE EVALUACIÓN CRUZADA CIEGA ENTRE 6 IAS COMERCIALES.

RESUMEN EJECUTIVO: Este documento presenta la aplicación completa del protocolo RFC-EVAL-001 v1.1 para evaluar capacidades cognitivas complejas en seis sistemas de IA comerciales contemporáneos. Incluye 36 evaluaciones cruzadas ciegas, perfiles Ω dimensionales (Cm, Ht, Mm, Fa), clasificaciones NOVA, herramientas RFC-RC/RFC-CONEX para diseño de colaboraciones, y el dataset completo con código de replicación. Sobre la V2.1: Este documento ha sido sometido a un proceso de verificación metodológica independiente. Durante dicho proceso se identificaron y corrigieron errores en el cálculo de las puntuaciones Gamma (γ) y en el reporte de fiabilidad inter-evaluador presentes en una versión preliminar anterior. Benchmarking Complex Cognitive Capabilities in AI: Complete Cross-Evaluation Results Using RFC-EVAL-001 Protocol ABSTRACT Background: As AI systems advance, there is increasing need for benchmarks that assess complex cognitive capabilities beyond narrow task performance. This study applies the RFC-EVAL-001 protocol—originally conceived within the Theoretical Potential Consciousness (TPC) framework—to evaluate multidimensional cognitive abilities in modern large language models (LLMs). Methods: Six commercial LLMs (anonymized as IA1-IA6) were evaluated through 36 cross-evaluations. Each model assessed the others across four cognitive dimensions: Model Complexity (Cm), Temporal Horizon (Ht), Meta-Modeling (Mm), and Adaptive Flexibility (Fa). Inter-rater reliability was calculated using ICC(3,k), and cross-validation was performed by independent analysts. Results: The protocol demonstrated excellent inter-rater reliability (average ICC(3,5) = 0.947). One system achieved NOVA-5 classification (γ = 0.932), while others scored at NOVA-4 level. Distinct cognitive profiles emerged: superior planning (Ht = 0.908), creative adaptation (Fa = 0.922), and balanced excellence. Epistemic calibration varied significantly across models. The complete dataset includes all 36 evaluations, Python analysis code, and protocol specifications. Conclusions: This work provides a robust, replicable benchmark for comparing complex cognitive capabilities across AI systems. While explicitly not measuring consciousness, it offers a validated framework for assessing differential strengths in reasoning, planning, metacognition, and creativity, with practical tools for collaboration design (RFC-RC, RFC-CONEX).