海豚：通过思考、实践和反馈进行闭环开放式自主研究

摘要

由于人工智能（AI）的发展，科学研究范式正在经历深刻的转变。最近的研究表明，各种AI辅助研究方法可以通过改善数据分析、加速计算和促进新颖思路的产生，大大提高研究效率。为了进一步实现最终目标（即自动科学研究），本文提出了Dolphin，这是第一个闭环开放式自动研究框架，以进一步构建整个人类科学研究过程。Dolphin能够生成研究思路，执行实验，并从实验结果中获得反馈，以生成更高质量的思路。具体而言，Dolphin首先基于按主题和任务属性排名的相关论文生成新颖思路。然后，代码会根据异常-回溯引导的本地代码结构进行自动生成和调试。最后，Dolphin会自动分析每个思路的结果，并将结果反馈给下一轮的思路生成。在不同主题的基准数据集上进行了实验，结果显示Dolphin能够持续生成新颖思路并在循环中完成实验。我们强调，Dolphin能够自动提出在某些任务中与最先进技术相媲美的方法，如2D图像分类和3D点分类。

English

The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods can largely improve research efficiency by improving data analysis, accelerating computation, and fostering novel idea generation. To further move towards the ultimate goal (i.e., automatic scientific research), in this paper, we propose Dolphin, the first closed-loop open-ended auto-research framework to further build the entire process of human scientific research. Dolphin can generate research ideas, perform experiments, and get feedback from experimental results to generate higher-quality ideas. More specifically, Dolphin first generates novel ideas based on relevant papers which are ranked by the topic and task attributes. Then, the codes are automatically generated and debugged with the exception-traceback-guided local code structure. Finally, Dolphin automatically analyzes the results of each idea and feeds the results back to the next round of idea generation. Experiments are conducted on the benchmark datasets of different topics and results show that Dolphin can generate novel ideas continuously and complete the experiment in a loop. We highlight that Dolphin can automatically propose methods that are comparable to the state-of-the-art in some tasks such as 2D image classification and 3D point classification.

海豚：通过思考、实践和反馈进行闭环开放式自主研究

Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback

摘要

Summary

Support