Core Architecture of a Unified Knowledge and Computational System: Inffer Map and the Generative Replica Model

This work presents a unified knowledge and computational system that enables direct human participation in large-scale pattern discovery. By representing knowledge as aligned structural relationships within a shared and continuously expanding space of perspectives, the system allows patterns to be constructed, explored, and connected across domains in a consistent and accessible manner. It supports both data-integrated and data-free exploration, bridging computational processes with human intuition and understanding. The system also establishes a practical pathway for interoperability between generative structural representations and existing computational models, enabling efficient application without loss of interpretability. In addition, it defines an operational approach for collective participation, through which learners can contribute, refine, and extend knowledge in a structured and scalable way. Crucially, this work identifies that the future scalability of pattern discovery is not limited by data or computation, but by the development of structurally trained individuals capable of direct contribution. As such, the unified knowledge system serves not only as a computational platform, but as a practical blueprint for education, collaboration, and human--AI co-evolution in pattern discovery.