美德机器：迈向通用人工智能科学

摘要

人工智能系统正通过加速特定研究任务——从蛋白质结构预测到材料设计——来变革科学发现，然而这些系统仍局限于狭窄的领域，需要大量的人工监督。科学文献的指数级增长和日益加深的领域专业化限制了研究人员跨学科综合知识并发展统一理论的能力，这促使人们探索更为通用的科学人工智能系统。本文展示了一种领域无关的、具有自主性的AI系统，它能够独立完成科学工作流程——从假设生成、数据收集到论文撰写。该系统自主设计并执行了三项关于视觉工作记忆、心理旋转和意象生动性的心理学研究，开展了一项涉及288名参与者的在线数据收集，通过超过8小时的连续编码开发了分析流程，并完成了论文的撰写。结果表明，AI科学发现流程能够进行具有理论推理和方法论严谨性的非平凡研究，其水平可与经验丰富的研究者相媲美，尽管在概念细微差别和理论解释方面仍存在局限。这是迈向能够通过现实世界实验验证假设的具身AI的一步，它通过自主探索科学领域中人类认知和资源限制可能忽视的区域，加速了科学发现。这一进展引发了关于科学理解本质及科学成果归属的重要问题。

English

Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. The exponential growth of scientific literature and increasing domain specialisation constrain researchers' capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for science. Here we show that a domain-agnostic, agentic AI system can independently navigate the scientific workflow - from hypothesis generation through data collection to manuscript preparation. The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness, executed one new online data collection with 288 participants, developed analysis pipelines through 8-hour+ continuous coding sessions, and produced completed manuscripts. The results demonstrate the capability of AI scientific discovery pipelines to conduct non-trivial research with theoretical reasoning and methodological rigour comparable to experienced researchers, though with limitations in conceptual nuance and theoretical interpretation. This is a step toward embodied AI that can test hypotheses through real-world experiments, accelerating discovery by autonomously exploring regions of scientific space that human cognitive and resource constraints might otherwise leave unexplored. It raises important questions about the nature of scientific understanding and the attribution of scientific credit.

美德机器：迈向通用人工智能科学

Virtuous Machines: Towards Artificial General Science

摘要

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