AbstractThis white paper introduces a novel Semantic Compression Architecture (SCA)designed to address fundamental inefficiencies in contemporary knowledge storage,retrieval, and inference systems. The architecture proposes a paradigm shift fromdata-centric to meaning-centric computation, wherein information is stored andprocessed at the level of semantic content rather than symbolic or bitwiserepresentation.The core innovation lies in a structured decomposition of meaning into four primaryaxes: Domain, Behaviour, Target, and Effect. This quadripartite structure enables thecreation of Semantic Vehicle Identification Numbers (Semantic VINs) that uniquelyidentify discrete units of meaning, facilitating redundancy elimination through semanticidentity matching rather than syntactic comparison.The architecture incorporates an energy-tiered processing model analogous to hybridvehicle power management, enabling deployment across diverse computationalcontexts from ultra-low-power edge devices to high-performance researchinfrastructure. This approach offers significant implications for sustainable computing,human-AI alignment, cross-linguistic knowledge representation, and thedemocratisation of intelligence systems.We present the theoretical foundations, architectural specifications, and productivityvaluations necessary for implementation, with particular attention to alignment withAustralian Government sustainability objectives and international open-sciencestandards.