Compressed Understanding Architecture: Knowledge Representation Through Structural Compression and Typed Graph Operations

This report presents the Compressed Understanding Architecture (CUA), a knowledge representation framework encoding understanding as irreducible seed definitions (150-200 bytes each), connected by 12 typed edge relationships, and amplified through triangle closing operations. Through 36 controlled experiments across 11 rounds, we demonstrate super-linear triangle scaling (T proportional to N^7.19), generation of 42,560 verifiable knowledge statements from 14.3 KB of storage, 70% offline independence from AI, and generalisation across 8 diverse domains (computer science, mathematics, logic, physics, biology, economics, philosophy, systems theory). All 30 empirical rules hold at 100 seeds. Designed for resource-constrained environments where compute and storage are limited. Full experimental code included for independent verification.