发表机构
Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过八次智能体算法探索试验及对8,420篇EDA论文的分析，发现EDA为算法研究提供了可执行环境，使算法探索变得廉价，并提出研究验证与奖励的新问题。
AI 中文摘要
作为EDA研究者，我们进行了八次有意识的智能体算法探索试验，选择了我们深度领域之外的几个主题。一位教师和七名学生参与了试验，其中包括没有发表经验的学生。在算法干预有限的情况下，智能体开发了数学构造，分析了现有工具，并实现了改进；一些努力未能达到其实际目标。我们还使用AI收集、分类并分析了2022-2026年间来自四个EDA会议和两个期刊的8,420篇论文。在2,380篇主要核心论文中，我们从标题和摘要中将97.7%分类为计算封闭的，包括关于新公式的工作。这些观察共同表明，EDA的许多领域为日益可及的算法研究提供了一个可执行环境。我们看到了工具开发者有机会去研究他们以前没有时间追求的想法。我们还询问，当结果变得比检查更容易产生时，EDA应如何验证和奖励研究，以及论文和场所标签将继续告诉我们关于贡献的什么信息。
英文摘要
As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth. One faculty member and seven students participated, including students without publication experience. With limited intervention in the algorithms, agents developed mathematical constructions, analyzed existing tools, and implemented improvements; some efforts fell short of their practical goals. We also used AI to collect, classify, and analyze 8,420 papers from four EDA conferences and two journals over 2022-2026. Among 2,380 primary-core papers, we classified 97.7% from titles and abstracts as computationally closed, including work on new formulations. Together, these observations suggest that much of EDA offers an executable environment for increasingly accessible algorithm research. We see an opportunity for tool developers to investigate ideas they previously lacked time to pursue. We also ask how EDA should validate and reward research when results become easier to produce than to examine, and what papers and venue labels will continue to tell us about a contribution.
Comments12 pages, 10 figures
