Semantic Compression as an Alignment-Preserving Interface for Code and Intelligence A Theory of Meaning-Layer Transmission Across Languages, Systems, and AI Models

AbstractSoftware redundancy reflects not inefficiency of coding, but inefficiency of representation.This paper introduces Semantic VINs (Vectorised Intent Nodes) as a universal meaninglayer for programming logic, enabling cross-language compression, equivalencedetection, and transformation without behavioural drift. Unlike token-based or AST-basedmodels, semantic compression preserves intent as structure, allowing deterministicrefactoring, translation, security propagation, and training data deduplication. Wedemonstrate theoretical compression ratios of 10:1 to 33:1 across diverse codebases byexploiting semantic invariance rather than symbolic redundancy. This architecture isfurther proposed as a foundation for alignment-aware AI cognition, replacing surfaceprediction with semantic reasoning. Critically, this version includes a formal anti-portfoliodocumenting the three structural boundaries where semantic compression provablyfails—establishing not only the capabilities but the valid operating envelope of theframework.