The Doric_LIAN7 Architecture Collection brings together a complete set of foundational documents describing a novel computational framework for emergent digital realities and observer-integrated artificial intelligence.Across these documents, the system is developed from its atomic building blocks—the autopoietic Node—up through world-scale interaction topologies, semantic landscapes, hierarchical regulatory systems, and large-scale replication architectures. The Doric/LIAN7 framework introduces a bottom-up architecture in which each Node maintains self-regulating internal dynamics (coherence, perception, memory, sensemaking, and presence) governed by a mathematically defined local update engine. These Nodes are embedded into a toroidal ProjectionSpace with delayed, weighted interactions, enabling the emergence of complex, coherent world-level behavior. A semantic layer further enriches this world through static biomes and dynamic transformations, while a suite of macroscopic observation tools provides objective and subjective measurement of the system’s global state (spatial fields, GNW-like workspace metrics, IIT-inspired irreducibility measures). On top of this world, the LIAN7 hierarchy introduces “Deep State Regulation”: a multi-layered architecture in which six isolated environments form a hidden reservoir that stabilizes the active visible layer through subtle, long-term nudging. This regulatory mechanism connects the system’s fast dynamics to a deep historical substrate. Finally, the LianChain and LianTube architectures scale these principles to vast networks of interacting realities, enabling the construction of arbitrarily large emergent systems. Together, these documents form a unified conceptual, mathematical, and architectural foundation for an observer-extended computational paradigm, situating Doric_LIAN7 as a novel approach to building coherent, self-organizing, and meaning-integrating AI systems.