Dolphin: Closed-loop Open-ended Auto-research through Thinking,
  Practice, and Feedback
Authors: name: Jiakang Yuan, name: Xiangchao Yan, name: Botian Shi, name: Tao Chen, name: Wanli Ouyang, name: Bo Zhang, name: Lei Bai, name: Yu Qiao, name: Bowen Zhou

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.