The Task-Based Methodology of Strong AI: Integrating LLMs, Logic Reasoning, and Multi-Blockchain Architectures

Andrey Nechesov

Keywords: Strong Artificial Intelligence, AGI, Large Language Models, LLMs, Multi-Blockchain Architecture, Metaverse, Task-Based Methodology, Multi-Agent Systems

TL;DR: A task-based framework integrating LLMs, logic engines, and multi-blockchains achieves ethically governed AGI through metaverse-validated reasoning, decentralized knowledge evolution, and unbounded cognitive growth.

Abstract: This paper introduces a task-based methodology for achieving Strong Artificial Intelligence (AGI) through the synergistic integration of Large Language Models (LLMs), logic-probabilistic reasoning, and multi-blockchain architectures. Addressing critical limitations of current LLM-centric systems—such as poor generalization in complex reasoning, outdated knowledge, and hallucinations—we propose a hybrid paradigm where LLMs generate hypotheses, symbolic logic engines ensure rigorous validation, and a hierarchical blockchain infrastructure enables secure, scalable knowledge evolution. Evaluated in a metaverse environment populated by heterogeneous agents, our framework demonstrates unbounded cognitive growth under computational constraints while maintaining interpretability and ethical alignment. Key innovations include a probabilistic knowledge hierarchy for explainable decisions, a decentralized multi-blockchain design for continuous learning, and a metaverse-based testbed for AGI safety and scalability. Theoretical guarantees of asymptotic cognitive scaling and practical applications in legal, scientific, and educational domains underscore the framework’s transformative potential.

Submission Number: 706