Conceptual Neural Networks 2.0: A Distributed Cognitive Architecture for Conceptual Reasoning, Controlled Neuroplasticity, Collaborative Intelligence, and Ethical Governance

Artificial intelligence has achieved significant progress in pattern recognition, language generation, multimodal processing, and automated problem-solving. However, the increasing capabilities of individual models do not necessarily translate into coherent, persistent, adaptable, or ethically governed artificial reasoning systems. This paper presents Conceptual Neural Networks 2.0 (CNN 2.0), an evolution of a conceptual architecture initially proposed to move beyond isolated statistical representations toward structured conceptual entities, explicit relationships, contextual reasoning, and ethical decision-making. Despite its name, CNN 2.0 is not a new neural network topology: Conceptual Neural Networks is the name of a cognitive architecture framework. CNN 2.0 extends the original conceptual framework into a distributed cognitive architecture composed of specialized agents, permanent and dynamic conceptual groups, a controlled orchestration mechanism, distributed memory, uncertainty management, autonomous inquiry and discovery, and controlled structural adaptation. Its central research question is how to design an artificial system that can not only reason, but also recognize the limits of its knowledge, investigate its own deficiencies, reorganize its capabilities, and remain subject to rules it cannot modify unilaterally. The paper makes two central contributions. The first is an explicit architectural separation between cognitive capability and operational authority, formalized as an authorization model with default deny, data-dependent risk classification, bounded human delegation, and enforcement by an Ethical Governance and Authorization Layer (EGAL) that operates outside the components it governs. The second is governed structural adaptation: a Cognitive Neuroplasticity Engine (CNE) proposes changes to agents, groups, and relationships, while a versioned Cognitive Genome separates a mutable structural layer from a protected normative layer and enforces explicit invariants. These contributions are integrated with a Cognitive Inquiry and Discovery Engine (CIDE), a Cognitive Uncertainty Manager (CUM), a Cognitive Link Manager (CLM), the Domain Factory, and a memory model with explicit epistemic status. Governance follows eleven principles of ethical resolution, organized as constitutional commitments, principles of evaluation and decision, and epistemic and procedural safeguards. The paper defines verifiable architectural properties, a threat model, an integrated operating scenario, the current implementation status in three production domains, and a validation methodology. The architecture adopts the Agent2Agent (A2A) protocol for communication between agents and adds two layers on top of it: verified capability profiles, measured by the network rather than declared by each agent, and EGAL authorization in front of every agent call. Quantum-inspired reasoning remains an optional research direction. The paper does not claim that the complete architecture has been implemented or experimentally validated: the governance and adaptation mechanisms (EGAL, CNE, and the Cognitive Genome) are specified but not yet implemented, the properties the paper states are design requirements until tested, and no performance advantage over existing architectures is claimed.