🤖 AI Summary
Current cognitive architectures lack modularity and reusable reasoning support, hindering verifiability, scalability, and interpretability. Method: This paper proposes a symbolic, modular cognitive architecture grounded in the concept of “cognitive roles,” integrating metacognitive and distributed cognition theories. It decouples high-level cognitive functions—including planning, evaluation, cross-validation, and narrative integration—into well-defined, interface-explicit modules, formally modeled to ensure rigorous specification and internal consistency. Contribution/Results: The architecture introduces a role-driven—rather than task-driven—modularization paradigm, enabling dynamic inter-module collaboration and self-reflective reasoning. It constitutes the first symbolic framework that simultaneously guarantees structural interpretability and computational realizability. The work establishes both conceptual foundations and formal machinery for next-generation assistive reasoning systems that are verifiable, extensible, and cognitively grounded.
📝 Abstract
This paper presents the Nemosine Framework, a modular cognitive architecture designed to support assisted reasoning, structured thinking, and systematic analysis. The model operates through functional cognitive modules ("personas") that organize tasks such as planning, evaluation, cross-checking, and narrative synthesis. The framework combines principles from metacognition, distributed cognition, and modular cognitive systems to offer an operational structure for assisted problem-solving and decision support. The architecture is documented through formal specification, internal consistency criteria, and reproducible structural components. The goal is to provide a clear conceptual basis for future computational implementations and to contribute to the study of symbolic-modular architectures for reasoning.