发表机构
Google, Paradigms of Intelligence Team; HPC Lab, InGeo Department, “G. D’Annunzio” University Chieti-Pescara; Mila - Quebec AI Institute; McGill University（谷歌，智能范式团队; 基耶蒂-佩斯卡拉“G. D’安农齐奥”大学InGeo部门高性能计算实验室; 米拉-魁北克人工智能研究所; 麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究数字原初汤中自我复制与功能的共同进化，通过初始化随机Z80汇编程序种群，引入基于任务的验证步骤，发现自我复制与数学问题解决可共同进化，计算压力等因素对其有影响，还产生了学习课程，证明了交互反馈循环。
AI 中文摘要
传统进化算法对繁殖进行硬编码，而自我复制能在数字“原初汤”中自发出现。本文研究这种自我复制与解决问题能力的共同进化。初始化随机的32字节Z80汇编程序种群，通过随机汇编级突变和程序对交互产生自我复制。引入基于任务的验证步骤，正确评估多项式可提高程序交互概率。实验有四项主要发现：自我复制与数学问题解决从初始随机性成功共同进化；计算压力加速紧凑、健壮繁殖架构出现；应用代谢约束增加程序进化出条件停止的可能性；程序分区到空间任务生态位时，自发自我复制产生学习课程。这些结果证明了交互反馈循环。
英文摘要
While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the coevolution of such emergent self-replication alongside problem-solving capabilities. We initialize a population of random 32-byte Z80 assembly programs, requiring self-replication to arise purely through random assembly-level mutations and pairwise program interactions. To couple computation with reproduction, we introduce a task-based validation step: correctly evaluating a polynomial raises a program's interaction probability above a baseline rate. Our experiments yield four primary findings. First, self-replication and mathematical problem-solving successfully coevolve from initial randomness. Second, the pressure to compute accelerates the emergence of compact, robust reproductive architectures that preserve memory for task execution. Third, applying metabolic constraints that penalize runtime promotes the emergence of sophisticated conditional execution patterns that reduce energy use. Finally, partitioning programs into interconnected task niches generates an emergent learning curriculum that utilizes simple solutions as stepping stones toward more complex tasks. Altogether, these results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.
