NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop
  System from Hypothesis to Verification
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.
682fe3a565bac3ec3556fc6c
NovelSeek Team
682fe3a565bac3ec3556fc6d
Bo Zhang
claimed_verified
2025-05-26T08:16:09.877Z
643dfd235aafbdca3a5792c0
/avatars/ce8553cf5936012c692e08054ee27937.svg
Bo Zhang
user
BoZhang
682fe3a565bac3ec3556fc6e
Shiyang Feng
682fe3a565bac3ec3556fc6f
Xiangchao Yan
682fe3a565bac3ec3556fc70
Jiakang Yuan
claimed_verified
2025-05-28T19:14:50.053Z
64a3d1ddb3239f3e3892b24b
/avatars/7ce585f5fc1d077fb1d70cc18c4da2c1.svg
Jiakang Yuan
user
JiakangYuan
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Zhiyin Yu
682fe3a565bac3ec3556fc72
Xiaohan He
682fe3a565bac3ec3556fc73
Songtao Huang
claimed_verified
2025-05-26T08:16:17.745Z
67cfd5f4d8cb8688d7e2df22
/avatars/099139aac6d803fa47579a1152da39ef.svg
Songtao Huang
user
huangst
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Shaowei Hou
682fe3a565bac3ec3556fc75
Zheng Nie
claimed_verified
2025-05-26T08:16:07.668Z
646d89a3534e52f8c307742b
/avatars/ca2b79eeaa6c9755dfced4245b2e2309.svg
niezheng
user
niez
682fe3a565bac3ec3556fc76
Zhilong Wang
claimed_verified
2025-05-26T08:16:12.187Z
640554f9ad54665351e0f28f
/avatars/a793a70c0767ba8938c7d808f0014504.svg
Zhilong Wang
user
bufan
682fe3a565bac3ec3556fc77
Jinyao Liu
682fe3a565bac3ec3556fc78
Runmin Ma
682fe3a565bac3ec3556fc79
Tianshuo Peng
682fe3a565bac3ec3556fc7a
Peng Ye
682fe3a565bac3ec3556fc7b
Dongzhan Zhou
682fe3a565bac3ec3556fc7c
Shufei Zhang
682fe3a565bac3ec3556fc7d
Xiaosong Wang
682fe3a565bac3ec3556fc7e
Yilan Zhang
682fe3a565bac3ec3556fc7f
Meng Li
682fe3a565bac3ec3556fc80
Zhongying Tu
682fe3a565bac3ec3556fc81
Xiangyu Yue
682fe3a565bac3ec3556fc82
Wangli Ouyang
682fe3a565bac3ec3556fc83
Bowen Zhou
682fe3a565bac3ec3556fc84
Lei Bai
NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop
  System from Hypothesis to Verification
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.