Atomic Units of X: Intelligence as Compression and Reuse

This paper introduces a theoretical framework that redefines intelligence as a process of "atomic compression and compositional reuse." It posits that diverse intelligent systems—cognitive, biological, computational, and organizational—achieve scalability by breaking down complex phenomena into fundamental, reusable "atomic units." These units can then be recombined to form higher-order structures. Drawing on insights from fields like cognitive science, information theory, and software engineering, the research develops the concept of atomic units as essential compression layers. These layers enhance efficiency, transferability, interpretability, and evolvability within a system. A central contribution is the "Compression Calculus," a formal method for comparing surface-level and atomic representations, and for describing how compression gains multiply across abstraction layers. This leads to the "Compounding Cascade thesis," suggesting that each new abstraction layer exponentially boosts representational efficiency. The paper critiques contemporary AI, arguing that many systems operate at suboptimal representation levels, relying on token or document processing rather than stable, concept-level atomic structures. It suggests that large language models are best viewed as "dynamic fusion engines" that navigate and recombine these atomic units. The framework aims to provide a foundation for designing self-evolving knowledge systems capable of discovering, refining, and composing new primitives, offering a unified perspective on expertise, knowledge representation, and the future of adaptive AI.