Integrating World Models with Multimodal Reasoning and Deep Introspection

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Authors: Santos Antonio Fraustro Solis
Cited by: 0

Abstract

Abstract We present NAOMA Evolutivo DIAL, an innovative architecture comprising 14 unified systems that integrates World Models with large-scale multimodal Language Models (LLMs) through deep introspection mechanisms, establishing a viable and testable path toward Artificial General Intelligence (AGI). The system proposes the synergistic integration of explicit reasoning (LLM), physical simulation (World Models), and introspective validation (DIAL) to produce emergent capabilities theoretically superior to current isolated systems. We formulate testable hypotheses regarding extended temporal coherence, explicit causal reasoning, and cross-domain generalization without task-specific retraining. We propose formal AGI benchmarks and posit that NAOMA DIAL can meet fundamental criteria: abstract reasoning, multi-horizon planning, knowledge transfer, and continuous self-improvement. This work establishes the theoretical and architectural framework, along with reproducible evaluation protocols for empirical validation Abstract Presentamos NAOMA Evolutivo DIAL, una arquitectura innovadora de 14 sistemas unificados que integra Modelos de Mundo (World Models) con Modelos de Lenguaje de Gran Escala (LLMs) multimodales mediante mecanismos de introspección profunda, estableciendo un camino investigable hacia la Inteligencia Artificial General (AGI). El sistema propone la integración sinérgica de razonamiento explícito (LLM), simulación física (World Models) y validación introspectiva (DIAL) para producir capacidades emergentes teóricamente superiores a sistemas aislados actuales. Formulamos hipótesis evaluables sobre coherencia temporal extendida, razonamiento causal explícito y generalización cross-domain sin reentrenamiento específico. Proponemos benchmarks formales para AGI y planteamos que NAOMA DIAL puede cumplir criterios fundamentales: razonamiento abstracto, planificación multi-horizonte, transferencia de conocimiento y auto-mejora continua. Este trabajo establece el marco teórico y arquitectural, con protocolos de evaluación reproducibles para validación empírica.

Fields of Study & Subjects

Psychiatry Mental Health Neuroscience AI-based Problem Solving and Planning Multimodal Machine Learning Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026