Dolphin: Closed-loop Open-ended Auto-research through Thinking,
  Practice, and Feedback
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.
677e3b1ddbff7b495e8f0424
Jiakang Yuan
admin_assigned
2025-01-08T13:52:54.625Z
64a3d1ddb3239f3e3892b24b
/avatars/7ce585f5fc1d077fb1d70cc18c4da2c1.svg
Jiakang Yuan
user
JiakangYuan
677e3b1ddbff7b495e8f0425
Xiangchao Yan
admin_assigned
2025-01-08T13:53:08.361Z
65b88b92e0bde92c176a888a
/avatars/fc1cb54328ca93860e97fc73a3c1eb2f.svg
Xiangchao Yan
user
yxc97
677e3b1ddbff7b495e8f0426
Botian Shi
admin_assigned
2025-01-08T13:53:16.461Z
643df87f7cd64d872cb9fabd
/avatars/c53bfabcee08de448dde973915e8b31d.svg
Botian Shi
user
friskit
677e3b1ddbff7b495e8f0427
Tao Chen
677e3b1ddbff7b495e8f0428
Wanli Ouyang
677e3b1ddbff7b495e8f0429
Bo Zhang
claimed_verified
2025-01-08T09:44:24.813Z
643dfd235aafbdca3a5792c0
/avatars/ce8553cf5936012c692e08054ee27937.svg
Bo Zhang
user
BoZhang
677e3b1ddbff7b495e8f042a
Lei Bai
677e3b1ddbff7b495e8f042b
Yu Qiao
677e3b1ddbff7b495e8f042c
Bowen Zhou
admin_assigned
2025-01-08T13:53:56.271Z
669f614b59adf5b56e05bce3
/avatars/ffd4189efbceb0e63a03db273065a44b.svg
BowenZhou
user
bowenZhou
Dolphin: Closed-loop Open-ended Auto-research through Thinking,
  Practice, and Feedback
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.
Dolphin is an auto-research framework that generates and tests scientific research ideas, comparing to state-of-the-art methods in image and point classification tasks.
closed-loop open-ended auto-research framework idea generation experiment automation exception-traceback-guided local code structure result analysis feedback loop 2D image classification 3D point classification