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The authors formulate a four-layer Cognitive Digital Twin (CDT) architecture鈥攕panning physical, digital-twin, cognitive, and task layers鈥攖hat moves beyond passive state mirroring toward an autonomous, closed-loop operational framework. While existing CDT implementations rely on ad-hoc integrations of LLMs and knowledge graphs, this system formalizes how operational feedback iteratively refines cognitive memory, attention mechanisms, and relational annotations across user-driven and self-driven modes. Simulation results demonstrate that this bidirectional feedback loop maintains task feasibility under constrained semantic communication while compounding operational efficiency over continuous deployment cycles.
Digital twins no longer need to be passive mirrors: structuring perception, memory, and task execution into a self-evolving closed loop enables cyber-physical systems to autonomously learn and optimize from operational feedback.
As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities into twin operation. Existing CDT studies often focus on specific enabling techniques, such as learning modules, knowledge graphs, and large language models, while providing limited insight into how cognition can be systematically integrated into DT architectures. To address this issue, this paper proposes a four-layer CDT architecture consisting of the physical layer, digital-twin layer, cognitive layer, and task layer. The proposed architecture establishes a self-evolving closed operational loop spanning these four layers, in which physical states are synchronized into digital representations, cognition constructs task-specific cognitive models through knowledge, memory, and attention, and task-level decisions are generated under practical constraints. Operational feedback further refines cognitive experience and updates relationships and annotations in the digital representation, enabling subsequent task interpretation, initiation, and reasoning to evolve with system operation. Based on this framework, two representative operation modes are characterized: user-request-driven cognition and self-driven cognition. We further discuss key enabling mechanisms and deployment challenges associated with semantic communication, knowledge querying, task orchestration, and closed-loop synchronization. A lightweight simulation study illustrates reliable closed-loop task feasibility under limited semantic information and improved operational efficiency through accumulated task experience. The proposed framework provides a structured foundation for the design and development of future CDT systems.
