Title: ZapGPT: Free-form Language Prompting for Simulated Cellular Control Show Chinese title

Title: ZapGPT：模拟细胞控制的自由格式语言提示

Abstract: Human language is one of the most expressive tools for conveying intent, yet most artificial or biological systems lack mechanisms to interpret or respond meaningfully to it. Bridging this gap could enable more natural forms of control over complex, decentralized systems. In AI and artificial life, recent work explores how language can specify high-level goals, but most systems still depend on engineered rewards, task-specific supervision, or rigid command sets, limiting generalization to novel instructions. Similar constraints apply in synthetic biology and bioengineering, where the locus of control is often genomic rather than environmental perturbation. A key open question is whether artificial or biological collectives can be guided by free-form natural language alone, without task-specific tuning or carefully designed evaluation metrics. We provide one possible answer here by showing, for the first time, that simple agents' collective behavior can be guided by free-form language prompts: one AI model transforms an imperative prompt into an intervention that is applied to simulated cells; a second AI model scores how well the prompt describes the resulting cellular dynamics; and the former AI model is evolved to improve the scores generated by the latter. Unlike previous work, our method does not require engineered fitness functions or domain-specific prompt design. We show that the evolved system generalizes to unseen prompts without retraining. By treating natural language as a control layer, the system suggests a future in which spoken or written prompts could direct computational, robotic, or biological systems to desired behaviors. This work provides a concrete step toward this vision of AI-biology partnerships, in which language replaces mathematical objective functions, fixed rules, and domain-specific programming. Show Chinese abstract

Abstract: 人类语言是表达意图最有力的工具之一，但大多数人工或生物系统缺乏解释或对它做出有意义响应的机制。弥合这一差距可以实现对复杂、去中心化系统的更自然的控制形式。在人工智能和人工生命领域，最近的研究探讨了语言如何指定高层次目标，但大多数系统仍然依赖于设计好的奖励、特定任务的监督或固定的命令集，这限制了对新指令的泛化能力。类似的限制也适用于合成生物学和生物工程领域，其中控制的焦点通常是基因组而非环境扰动。一个关键的开放问题是，人工或生物群体是否可以在没有特定任务调整或精心设计的评估指标的情况下，仅通过自由形式的自然语言进行引导。我们在这里提供了一个可能的答案，首次展示了简单的智能体集体行为可以通过自由形式的语言提示来引导：一个人工智能模型将祈使句提示转换为对模拟细胞的干预；第二个人工智能模型评估提示描述结果细胞动力学的效果；前者人工智能模型被进化以提高后者生成的评分。与之前的工作不同，我们的方法不需要设计好的适应度函数或领域特定的提示设计。我们展示了进化后的系统在未经重新训练的情况下能够推广到未见过的提示。通过将自然语言视为一个控制层，该系统暗示了一个未来，在这个未来中，口头或书面的提示可以指导计算、机器人或生物系统实现期望的行为。这项工作为人工智能与生物学合作的愿景迈出了一步，其中语言取代了数学目标函数、固定规则和领域特定的编程。