Recursive self-design refers to AI-assisted modification of the mechanisms by which an AI system is built, evaluated, and improved. This paper treats MetaAI not as a mature paradigm, but as a working term for a human-seeded, AI-expanded development pattern in which the design space itself becomes a target of modification. We propose an operational evidence framework with four criteria: inspectable target system, meta-level modifier, feedback-directed selection, and recursive continuation. We then map public systems, including Darwin Goedel Machine (DGM), STOP, Goedel Agent, and ShinkaEvolve, against these criteria. DGM provides the most direct currently reported evidence: its published results show improvement from 20% to 50% on SWE-bench Verified and from 14.2% to 30.7% on full Polyglot after 80 iterations, with ablations suggesting that both open-ended exploration and self-improvement contribute. Finally, we provide MetaAI-Mini, a reproducible HumanEval-based protocol and codebase. Because no completed model run is included in this build, MetaAI-Mini is reported as a protocol rather than as an experimental result.

翻译：递归自我设计指人工智能辅助修改AI系统构建、评估和改进机制的过程。本文将MetaAI视为一种以人类为种子、由AI扩展的开发模式，其设计空间本身成为修改目标，而非成熟范式。我们提出一个包含四项准则的操作性证据框架：可检查目标系统、元层级修改器、反馈导向选择和递归延续。随后，我们对照这些准则映射了公开系统，包括Darwin Goedel Machine (DGM)、STOP、Goedel Agent和ShinkaEvolve。其中，DGM提供了目前最直接的已报告证据：其发表结果显示，经过80次迭代，在SWE-bench Verified上性能从20%提升至50%，在完整Polyglot上从14.2%提升至30.7%，消融实验表明开放式探索与自我改进均有贡献。最后，我们提供了MetaAI-Mini——一个基于HumanEval的可复现协议与代码库。由于本构建中未包含完整的模型运行，MetaAI-Mini作为协议而非实验结果进行报告。