AI可自主开展科学探索，需重构机构以保障其可信与安全。
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
- 构建多智能体系统，让AI自主完成文献分析到模型验证全流程。
- 原型框架Denario加速发现周期，探索人类无法触及的模型空间。
- 适合关注AI科研伦理、制度设计的研究者与政策制定者。
具有代理能力的人工智能系统正开始辅助、加速并部分自动化科学发现，涵盖文献综述、代码生成、数据分析、假说提出与模型批判等任务。我们认为这一转变是质变而非量变，经过合理设计的多智能体系统可能演变为‘AI科学家’，显著拓展科学的假说生成与验证能力。此类系统必须在适配的科学生态中开发与部署：机构需重新设计以确保验证性、问责性、可解释性及双重用途安全。本文以原型框架Denario为例，展示多智能体架构如何加速发现周期并探索人类难以触及的模型空间；探讨其对署名权、同行评审及人类科学家角色的深远影响；最后提出应将AI视为认知主体而非单纯工具进行治理。
原文摘要 · Abstract (English)
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.
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