This article introduces Recursivism as a conceptual framework for analyzing contemporary artistic practices in the age of artificial intelligence. While recursion is precisely defined in mathematics and computer science, it has not previously been formalized as an aesthetic paradigm. Recursivism designates practices in which not only outputs vary over time, but in which the generative process itself becomes capable of reflexive modification through its own effects. The paper develops a five-level analytical scale distinguishing simple iteration, cumulative iteration, parametric recursion, reflexive recursion, and meta-recursion. This scale clarifies the threshold at which a system shifts from variation within a fixed rule to genuine self-modification of the rule itself. From this perspective, art history is reinterpreted as a recursive dynamic alternating between internal recursion within movements and meta-recursive transformations of their generative principles. Artificial intelligence renders this logic technically explicit through learning loops, parameter updates, and code-level self-modification. To distinguish Recursivism from related notions such as generative art, cybernetics, process art, and evolutionary art, the article proposes three operational criteria: state memory, rule evolvability, and reflexive visibility. These concepts are examined through case studies including Refik Anadol, Sougwen Chung, Karl Sims, and the Darwin-Godel Machine. The article concludes by examining the aesthetic, curatorial, and ethical implications of self-modifying artistic systems.

翻译：本文提出“递归主义”作为分析人工智能时代当代艺术实践的概念框架。尽管递归在数学和计算机科学中有精确定义，但此前尚未被形式化为一种美学范式。递归主义指代这样一类实践：不仅其输出随时间变化，其生成过程本身还能通过自身效应实现反身性修改。本文构建了一个包含五个层级的分析尺度，区分简单迭代、累积迭代、参数递归、反身递归与元递归。该尺度明确了系统从固定规则内的变异转向规则本身真正自我修改的临界点。由此视角出发，艺术史被重新阐释为一种递归动态，交替着运动内部的递归与生成原则的元递归转化。人工智能通过学习循环、参数更新和代码级自我修改，使这种逻辑在技术上显性化。为区分递归主义与生成艺术、控制论、过程艺术及演化艺术等相关概念，本文提出三项操作标准：状态记忆、规则可演化性与反身可见性。这些概念通过包括Refik Anadol、Sougwen Chung、Karl Sims及达尔文-哥德尔机在内的案例研究进行检验。文章最后探讨了自我修改艺术系统的美学、策展与伦理意涵。