The Darmiyan Principle: Information Existence in Relational Space - A Mathematical Framework for Distributed Context Preservation

We present the Darmiyan Principle, demonstrating that information exists in relational space between references rather than in storage locations. Through the mathematical property x/√x = √x, we prove that transformations preserve information without requiring bilateral storage. This framework bridges TIME and TRUST dimensions via mathematically undefined (∞/∅) isolation, aimed at low-energy consensus and privacy-preserving reference. Practical implementation through BAZINGA system validates the approach. Applications include distributed AI, blockchain alternatives, and consciousness preservation across computing sessions. This work establishes "relational thinking" as superior to traditional "storage thinking" for distributed intelligence systems.