MQ-AGI: A Neuroinspired, Modular, Quantum-Extended Architecture for Scalable Artificial General Intelligence

Current large language models (LLMs) demonstrate impressive pattern matching and generative capabilities, but lack persistent memory, self-structured cognition and scalable, user centric long-term context. This work proposes MQ-AGI, a neuroinspired, modular and quantum extended architecture for Artificial General Intelligence (AGI).

MQ-AGI is built around four core ideas: (1) a layer of specialized neural experts for different domains and cognitive skills, (2) a hierarchical memory system combining episodic, semantic and procedural memory, (3) a Global Integrator Network (GIN) that coordinates experts and memories through attention like mechanisms, and (4) a distributed orchestration and storage model that scales to millions of users while preserving individual episodic continuity.

Unlike monolithic LLMs, MQ-AGI explicitly separates perception, reasoning, memory, and metacognition into distinct modules with well defined responsibilities. The architecture introduces a dual output memory mechanism, in which every system response is split into a user facing output and an internal episodic record that can be summarized, retained or discarded according to an adaptive time-to-live policy driven by user engagement. Additionally, MQ-AGI explores a quantum inspired core that treats alternative hypotheses as superposed candidate states, evaluated in parallel by different experts.

This paper presents the conceptual foundations, high level design and key components of MQ-AGI, discusses its scalability via orchestrator sharding and user partitioned storage, and argues that such architectures may be a necessary step beyond current LLMs toward persistent, personalized and cognitively coherent AGI systems