A Theoretical Framework for AGI Based on the Synergistic Operation of Multiple Modules-From human,To the infinite

In recent years, the emergence of large language models (LLMs) has greatly advanced AGI research, revealing their potential for tasks ranging from natural language understanding to complex reasoning. This paper presents a theoretical framework for LLM-based knowledge-injection neural networks, achieving comprehensive AGI functionality through the collaborative interaction of seven modules: the Perception Module (based on few-shot learning and change-driven sensory entropy), the Logical Reasoning Module (supporting natural language logical mapping and dynamic decision-making), the Autonomous Consciousness Module (based on Bayesian generalization and behavioral reflection), the Metric Module (dynamically assessing commonalities and differences), the Memory/Knowledge Module (storing unique memories and experiences while integrating LLM with internet-based learning), the Emotion Module (constructing emotions and values through hormone-driven mechanisms), and the Physical Environment Simulation Module (modeling environmental sequences and conducting virtual experiments). Bidirectional LLM mapping facilitates communication between modules, and all modules incorporate internet-based self-learning to handle new tasks. Key innovations include natural language-based logical reasoning, hormone-driven desire mechanisms, dynamic metric functions for common feature extraction, change-driven perception prioritization, consequence prediction through sensory sequences, cognitive leaps triggered by free will, adaptive self-learning through the internet, and modular design prospects. Through detailed theoretical elaboration and interdisciplinary perspectives, this work provides a comprehensive foundation for constructing a complete AGI entity.