AI科学家任务控制（AIMC）：面向自主科学发现的人类监督的可视分析

中文摘要 AI 辅助

自主科学发现系统可在极少人类干预下生成大量研究思路、实验及手稿。随着这类系统能力不断提升，科学家需要有效机制来监控输出质量、识别反复出现的失败模式、理解研究演化，并优先选择有前景的发现进行评审。我们提出AIMC，这是一个用于自主科学发现人类监督的可视分析框架。AIMC结合语义嵌入、自动弱点提取、时间分析及交互式可视化，以支持对AI生成的研究制品的探索。我们通过对自主AI科学家（FARS）生成的论文及其相关评审反馈的案例研究来演示该框架。我们的分析揭示了反复出现的方法学弱点、不断演化的研究主题、特定领域的质量差异，以及一小部分值得人类深入检查的高度新颖的论文。这些发现表明，可视分析可如何支持新兴自主科学发现工作流中的透明度、诊断及人机协作。

英文摘要

Autonomous scientific discovery systems can generate large numbers of research ideas, experiments, and manuscripts with minimal human intervention. As these systems become increasingly capable, scientists require effective mechanisms to monitor output quality, identify recurring failure modes, understand research evolution, and prioritize promising discoveries for review. We present AIMC, a visual analytics framework for human oversight of autonomous scientific discovery. AIMC combines semantic embeddings, automated weakness extraction, temporal analysis, and interactive visualizations to support the exploration of AI-generated research artifacts. We demonstrate the framework through a case study of the papers generated by an autonomous AI Scientist (FARS), together with their associated review feedback. Our analysis reveals recurring methodological weaknesses, evolving research themes, domain-specific differences in quality, and a small set of highly novel papers that warrant deeper human inspection. These findings illustrate how visual analytics can support transparency, diagnosis, and human AI collaboration in emerging autonomous scientific discovery workflows.

发表机构

• Stony Brook University（石溪大学）

机构由 AI 辅助整理，请以论文原文为准。