While modern large language models (LLMs) have achieved remarkable feats in natural language processing, their architecture, even when augmented with retrieval systems like GraphRAG or KAG, falls short of true artificial general intelligence (AGI). The transition from a sophisticated knowledge-retrieval engine to a cognitive entity requires a fundamental shift in design, moving from a static, query-response system to a dynamic, self-improving architecture. AGI would not simply recall information; it would reason, learn, and adapt continuously, mirroring the complexity of human cognition.
A key limitation of current architectures is the separation between retrieval and generation. To achieve AGI, the retriever would need to evolve into a retriever-reasoner. This component would not merely fetch data from a knowledge graph but would actively perform inference on the retrieved information. It would synthesize new knowledge, identify inconsistencies, and draw logical conclusions before passing the refined information to the core language model. This process would be akin to a human's ability to recall fragmented memories and mentally piece them together to form a coherent understanding.
Central to this cognitive architecture would be a dual-memory system. A short-term memory (STM) would be responsible for maintaining the immediate context of a conversation or a problem-solving session. This allows for fluid, real-time responses. Concurrently, a robust long-term memory (LTM) would serve as the system's cumulative knowledge base. The LTM would be continuously updated with new insights and beliefs derived from the retriever-reasoner's analysis. This active integration of new knowledge, facilitated by representation learning, would allow the system to truly learn rather than just remember. Knowledge would be elucidated not as raw text, but as a rich, semantic graph where concepts are interconnected and relationships are explicitly defined.
Furthermore, a truly general intelligence would require the ability to learn continuously and autonomously. A static, one-time training process is insufficient. This could be achieved through a federated learning model where specialized agents, each focused on a different domain or task, could continuously learn from their experiences. These agents would then share their updated models or knowledge representations in a secure, decentralized manner, leading to a collective intelligence that grows exponentially. The inference process would be ubiquitous, occurring not just during a user query but also internally as the system refines its world model and consolidates new information.
Ultimately, this architecture begins to truly inspire cognitive mechanisms in comparison to a human. The STM/LTM duality mirrors our working and declarative memory. The sophisticated retriever-reasoner acts like our hippocampus and frontal cortex, retrieving and reasoning over past experiences. The continuous learning and knowledge elucidation through representation learning emulate our own neuroplasticity and ability to form new, abstract concepts. This system moves beyond a powerful database to become a living, reasoning entity, capable of thought, learning, and self-improvement on a scale that surpasses anything seen today.