With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial-and-error to discover the configurations of lifelike simulations. This paper presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called Automated Search for Artificial Life (ASAL), (1) finds simulations that produce target phenomena, (2) discovers simulations that generate temporally open-ended novelty, and (3) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids lifeforms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.

翻译：随着近期诺贝尔奖授予蛋白质发现领域的突破性进展，探索大型组合空间的基础模型有望彻底变革众多科学领域。人工生命研究尚未整合基础模型，这为该领域提供了重要机遇，以减轻长期以来主要依赖人工设计和试错来发现类生命模拟配置的历史负担。本文首次通过视觉-语言基础模型成功实现了这一机遇。所提出的方法称为"人工生命自动化搜索"，能够：(1) 找到产生目标现象的模拟系统，(2) 发现能生成时间维度开放式新颖性的模拟系统，(3) 揭示整个具有趣味多样性的模拟空间。得益于基础模型的通用性，该方法在多种人工生命基底中均能有效工作，包括Boids、粒子生命、生命游戏、Lenia和神经细胞自动机。该技术潜力的一个重要例证是发现了前所未见的Lenia和Boids生命形态，以及类似康威生命游戏那样具有开放式演化特征的细胞自动机。此外，基础模型的使用使得能够以人类可理解的方式对先前只能定性描述的现象进行量化。这一新范式有望将人工生命研究推进到超越单纯依赖人类创造力的新境界。