发表机构
SeKondBrain AI Labs（第二大脑人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文提出将智能视为原子压缩与组合复用过程的理论框架，借助多学科证据阐述原子单位概念，给出压缩演算等核心方法，为设计自进化知识系统奠基，为智能相关多方面提供统一视角。
AI 中文摘要
本文提出一个理论框架，将智能理解为原子压缩和组合复用的过程。我们认为认知、生物、计算和组织系统通过将复杂现象分解为可复用的原子单位来实现可扩展智能，这些原子单位可重组为更高阶结构。借助多学科证据，本文提出原子单位概念作为支持效率、转移、可解释性和可进化性的基本压缩层。核心贡献是压缩演算，用于比较表层表示与原子表示及描述压缩增益如何在抽象层复合。还介绍复合级联论点，即每增加一层抽象，表征效率成倍增加。本文指出当代人工智能系统常处于次优表征水平，大语言模型应理解为原子单位的动态融合引擎。该框架为设计自进化知识系统奠定基础，通过将智能重构为组合抽象的压缩，为专业知识、知识表示、可解释人工智能及自适应智能系统未来架构提供统一视角。
英文摘要
This paper proposes a theoretical and empirical framework for understanding intelligence as a process of atomic compression and compositional reuse. It argues that scalable cognitive, biological, computational, and organisational systems reduce complexity by organising information into reusable units that can be recombined into higher-order structures. The central contribution is the Compression Calculus, a formal framework for comparing surface evidence with atomic representations and for describing how abstraction can compound across layers. The framework is evaluated on large, multi-source software corpora under an open-world concept model, showing substantial evidence-to-concept consolidation in two large projects, while a smaller third project remains below the target threshold. The analysis further identifies important boundary conditions: consolidation depends on corpus scale, evidence-unit definition, and concept identity, and the observed reduction is driven primarily by within-corpus recurrence rather than cross-source recurrence. The paper also develops a representational account of the meaning gap in contemporary generative systems, describing latent, context-dependent conceptual approximations as soft atoms that lack the persistent identity and compositional constraints of stable atomic units. This motivates an architectural view in which large language models function as dynamic fusion engines that navigate and compose persistent conceptual structures rather than serving as the sole repository of those structures.
