The AGI-Powered Metaverse: A Decentralized Task-Based Cognitive Architecture

The pursuit of Strong Artificial Intelligence (AGI) is hindered by the fundamental limitations of Large Language Models (LLMs), such as hallucinations, static knowledge, and a lack of verifiable reasoning. This paper proposes a hybrid, decentralized cognitive architecture for an AGI-powered Metaverse that addresses these challenges. The framework with Task-Based Cognitive Architecture (TBCA) establishes a closed cognitive loop integrating three core components: an LLM for creative hypothesis generation (“System 1”), a symbolic logic engine for rigorous verification (“System 2”), and a decentralized multi-blockchain knowledge base serving as a secure long-term memory for collective intelligence. We demonstrate within a simulated Metaverse environment that this architecture supports cumulative learning and solves complex tasks, such as legal reasoning, which are intractable for isolated LLMs. The key innovations include a probabilistic knowledge hierarchy for explainable reasoning and a multi-blockchain structure that ensures tamper-proof knowledge sharing among multiple AI agents. This approach provides a foundational framework for verifiable and cumulative learning in dynamic multi-agent environments, paving the way for transparent, robust, and continuously evolving AI systems within the Metaverse.