NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop
  System from Hypothesis to Verification
Authors: name: NovelSeek Team, name: Bo Zhang, name: Shiyang Feng, name: Xiangchao Yan, name: Jiakang Yuan, name: Zhiyin Yu, name: Xiaohan He, name: Songtao Huang, name: Shaowei Hou, name: Zheng Nie, name: Zhilong Wang, name: Jinyao Liu, name: Runmin Ma, name: Tianshuo Peng, name: Peng Ye, name: Dongzhan Zhou, name: Shufei Zhang, name: Xiaosong Wang, name: Yilan Zhang, name: Meng Li, name: Zhongying Tu, name: Xiangyu Yue, name: Wangli Ouyang, name: Bowen Zhou, name: Lei Bai

Artificial Intelligence (AI) is accelerating the transformation of scientific
research paradigms, not only enhancing research efficiency but also driving
innovation. We introduce NovelSeek, a unified closed-loop multi-agent framework
to conduct Autonomous Scientific Research (ASR) across various scientific
research fields, enabling researchers to tackle complicated problems in these
fields with unprecedented speed and precision. NovelSeek highlights three key
advantages: 1) Scalability: NovelSeek has demonstrated its versatility across
12 scientific research tasks, capable of generating innovative ideas to enhance
the performance of baseline code. 2) Interactivity: NovelSeek provides an
interface for human expert feedback and multi-agent interaction in automated
end-to-end processes, allowing for the seamless integration of domain expert
knowledge. 3) Efficiency: NovelSeek has achieved promising performance gains in
several scientific fields with significantly less time cost compared to human
efforts. For instance, in reaction yield prediction, it increased from 27.6% to
35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from
0.52 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation,
precision advanced from 78.8% to 81.0% in a mere 30 hours.