Command and control (C2) has traditionally been framed as a military problem of sensing, deciding, coordinating, and acting under uncertainty, conflict, and time pressure. In the agentic age, however, this problem is being extended far beyond the battlefield, as human organizations become increasingly embedded in hybrid environments composed of artificial agents, digital twins, social sensing networks, foundation models, autonomous platforms, and cyber–physical–social institutions. This article develops a conceptual framework for parallel science and technology for C2 by revisiting the historical mission of C2 as a quest for certainty under diversity, uncertainty, and complexity (DUC), and by tracing its transformation toward focus, agility, and convergence (FAC). Building on parallel systems, artificial societies, computational experiments, parallel execution (ACP), cyber–physical–social systems (CPSS), C2 management (C2M), planning–readiness–execution–assessment (PREA) loops, social cognition operations, and parallel battlefield intelligence, the article argues that C2 should be reframed as a general science of coordinated adaptation. It further examines emerging agentic societies as a new operating ecology, explains why parallel science and technology are needed, and redefines the mission of future C2 around sustainability and resiliency. Rather than merely accelerating decision cycles or optimizing combat efficiency, parallel C2 aims to support anticipation, adaptation, trust, recovery, and responsible human agency across both military and civilian domains, thereby transforming command and control from post-hoc operational control into a science of sustainable and resilient coordination in the agentic age.

Recently, with the rapid and powerful development of artificial intelligence technology, a major paradigm shift of educational models —parallel education reform supported by intelligent technology has begun to spread across the world, which commands a comprehensive overview and reflection of current education and teaching from the perspective of parallel intelligence and parallel education. For this purpose, this paper firstly discussed the origin and goal of education and proposed a new prospect to consider and analyze the relationship between parallel intelligence and parallel education. Then, the definition, basic framework and application process of parallel intelligence and parallel education was introduced, which highlighted pointed the fact and significance that parallel intelligence provided effective intelligent technology support for parallel education. After that, we focused on the parallel education reform enabled by intelligent education, and held that this reform not only included the reforms of teaching content and connotation, subject structure, and assessment method, but also involved the innovation of education form. Finally, looking forward to the future, it was emphasized that the concepts of parallel teachers and students, parallel schools and related technologies should be deployed to ensure the success of education reform, so as to create a new direction of education improvement and seek sustainable human welfare.

Issues related to the impact of ChatGPT-like artificial intelligence generated content (AIGC) and artificial general intelligence (AGI) technologies on medicine and medicare have been presented and discussed. We believe that advances in medical foundation models, scenarios engineering, and medical operations with operating systems would lead to parallel doctors in parallel hospitals, i.e., digital, robotic, and human doctors working in parallel within cyber-physical-social spaces under three modes: autonomous, parallel, and expert/emergency operations. Under decentralized/distributed autonomous organizations/operations (DAO) and DeSci, as well as DeMed and DeHospitals, the vision for individual patients with her/his personalized digital hospital could be a reality in the future.

Abnormal or drastic changes in the natural environment may lead to unexpected events, such as tsunamis and earthquakes, which are becoming a major threat to national economy. Currently, no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster occurs. In this study, we propose a social evolution modeling approach and construct a deduction model for self-playing, self-learning, and self-upgrading on the basis of the idea of parallel data and reinforcement learning. The proposed approach can evaluate the impact of an event, deduce the situation, and provide optimal strategies for decision-making. Taking the breakage of a submarine cable caused by earthquake as an example, we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.