Scientific knowledge is increasingly becoming part of the operational substrate of intelligent systems and intelligent industries, rather than serving only as a record of scholarly communication. As these systems move toward closed-loop integration of perception, decision, and control, the infrastructures that produce, validate, update, and circulate knowledge become correspondingly important. Yet contemporary scientific publishing still relies on static articles, delayed review cycles, document-centered dissemination, and weak machine readability, creating a knowledge-automation bottleneck for agent-based intelligence. Drawing on parallel intelligence and the artificial systems, computational experiments, and parallel execution (ACP) approach, this letter proposes parallel publishing: a living knowledge infrastructure that runs in parallel with physical, cyber, and social systems, continuously transforming research signals, operational feedback, and scientific claims into structured, verifiable, and machine-usable knowledge units. A three-layer architecture is presented, together with a conceptual representation of structured knowledge units and four defining design properties: continuity, structured verifiability, incentive alignment, and decentralized resilience. An autonomous-driving scenario further illustrates how operational feedback can be converted into verifiable knowledge for intelligent systems. The analysis suggests that closing the intelligence loop depends not only on advances in models, sensors, and hardware, but also on the emergence of a knowledge layer that is current, auditable, and structured for machine use across intelligent industries and societies.