EL-RAKHAWI AXIOMATIC CAUSAL AND FORMAL VERIFICATION FRAMEWORK (EACFV) A Unified Paradigm for Hallucination-Free, Human-Like Understanding, and Mathematically Guaranteed Problem Solving in Artificial Intelligence
Identification
- Clé publique
- radar:r-b6c332ff1e721564108cd678b0c4c072
- Auteurs
- m el-rakhawi
- Année déclarée
- 2026
- Langue déclarée
- en
- Type source
- book
- Première détection
- 2026-11-14T02:48:18.637838+00:00
- Dernière observation des métadonnées
- 2026-11-14T02:48:18.637838+00:00
- Dernière vérification HTTP
- Non effectuée
Résumé documentaire — langue originale
The El-Rakhawi Axiomatic Causal and Formal Verification Framework (EACFV) represents the culmination of artificial general intelligence research, addressing the fundamental limitations of current AI systems through a neuro-symbolic architecture that eliminates hallucination, achieves human-like causal understanding, and provides mathematically guaranteed problem-solving. EACFV operates through three integrated pillars. First, the Formal Verification Engine with Neural Autoformalization translates natural language into formal proof languages (Lean 4/Coq), strictly gated by deterministic theorem provers—ensuring only formally verified truths are accepted. Second, the Causal World Model employs Neural Causal Discovery algorithms to learn structural causal models from observational data, enabling rigorous counterfactual reasoning through do-calculus rather than mere statistical correlation. Third, the Neuro-Symbolic Hybrid Solver combines neural intuition with symbolic rigor, utilizing Process Reward Models (PRMs) to provide dense intermediate feedback during proof search, overcoming the sparse reward problem that plagues traditional approaches. Crucially, EACFV embodies epistemic humility by respecting Gödel's Incompleteness Theorems and Turing's Halting Problem. Rather than hallucinating solutions to undecidable problems, the system formally certifies computational boundaries—proving independence within specified axiomatic systems or declaring resource-bounded search exhaustion. This epistemic safety distinguishes true intelligence from statistical mimicry. Integrating PTIQCS for quantum-accelerated proof search, OSRF for modeling biological intuition, and AQBI for continuous causal graph updates, EACFV transforms AI from a deceptive predictor into a truthful intellectual partner, democratizing mathematical discovery while maintaining absolute logical integrity.
Identifiants
- OPENALEX
- https://openalex.org/W7223566276
- DOI
- 10.5281/zenodo.23644669
Repères documentaires
- Famille documentaire
- Livre
- Types déclarés par les sources
- book
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Description bibliographique du livre
- Date de publication déclarée
- 2026-10-25
Disponibilité non établie
Sources
OpenAlex · Document 1
Citer cette référence
m el-rakhawi. EL-RAKHAWI AXIOMATIC CAUSAL AND FORMAL VERIFICATION FRAMEWORK (EACFV) A Unified Paradigm for Hallucination-Free, Human-Like Understanding, and Mathematically Guaranteed Problem Solving in Artificial Intelligence. 2026. https://doi.org/10.5281/zenodo.23644669
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- APA
- m el-rakhawi. (2026). EL-RAKHAWI AXIOMATIC CAUSAL AND FORMAL VERIFICATION FRAMEWORK (EACFV) A Unified Paradigm for Hallucination-Free, Human-Like Understanding, and Mathematically Guaranteed Problem Solving in Artificial Intelligence. https://doi.org/10.5281/zenodo.23644669
- Chicago
- m el-rakhawi. EL-RAKHAWI AXIOMATIC CAUSAL AND FORMAL VERIFICATION FRAMEWORK (EACFV) A Unified Paradigm for Hallucination-Free, Human-Like Understanding, and Mathematically Guaranteed Problem Solving in Artificial Intelligence. 2026. https://doi.org/10.5281/zenodo.23644669
- ISO 690
- m el-rakhawi. EL-RAKHAWI AXIOMATIC CAUSAL AND FORMAL VERIFICATION FRAMEWORK (EACFV) A Unified Paradigm for Hallucination-Free, Human-Like Understanding, and Mathematically Guaranteed Problem Solving in Artificial Intelligence. 2026. https://doi.org/10.5281/zenodo.23644669
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Source bibliographique / publication d’origine · https://doi.org/10.5281/zenodo.23644669
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