title: NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop
    System from Hypothesis to Verification
summary: 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.
paper_authors._id: 682fe3a565bac3ec3556fc6c
paper_authors.name: NovelSeek Team
paper_authors._id: 682fe3a565bac3ec3556fc6d
paper_authors.name: Bo Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:09.877Z
paper_authors._id: 643dfd235aafbdca3a5792c0
paper_authors.avatarUrl: /avatars/ce8553cf5936012c692e08054ee27937.svg
paper_authors.fullname: Bo Zhang
paper_authors.type: user
paper_authors.user: BoZhang
paper_authors._id: 682fe3a565bac3ec3556fc6e
paper_authors.name: Shiyang Feng
paper_authors._id: 682fe3a565bac3ec3556fc6f
paper_authors.name: Xiangchao Yan
paper_authors._id: 682fe3a565bac3ec3556fc70
paper_authors.name: Jiakang Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T19:14:50.053Z
paper_authors._id: 64a3d1ddb3239f3e3892b24b
paper_authors.avatarUrl: /avatars/7ce585f5fc1d077fb1d70cc18c4da2c1.svg
paper_authors.fullname: Jiakang Yuan
paper_authors.type: user
paper_authors.user: JiakangYuan
paper_authors._id: 682fe3a565bac3ec3556fc71
paper_authors.name: Zhiyin Yu
paper_authors._id: 682fe3a565bac3ec3556fc72
paper_authors.name: Xiaohan He
paper_authors._id: 682fe3a565bac3ec3556fc73
paper_authors.name: Songtao Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:17.745Z
paper_authors._id: 67cfd5f4d8cb8688d7e2df22
paper_authors.avatarUrl: /avatars/099139aac6d803fa47579a1152da39ef.svg
paper_authors.fullname: Songtao Huang
paper_authors.type: user
paper_authors.user: huangst
paper_authors._id: 682fe3a565bac3ec3556fc74
paper_authors.name: Shaowei Hou
paper_authors._id: 682fe3a565bac3ec3556fc75
paper_authors.name: Zheng Nie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:07.668Z
paper_authors._id: 646d89a3534e52f8c307742b
paper_authors.avatarUrl: /avatars/ca2b79eeaa6c9755dfced4245b2e2309.svg
paper_authors.fullname: niezheng
paper_authors.type: user
paper_authors.user: niez
paper_authors._id: 682fe3a565bac3ec3556fc76
paper_authors.name: Zhilong Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:12.187Z
paper_authors._id: 640554f9ad54665351e0f28f
paper_authors.avatarUrl: /avatars/a793a70c0767ba8938c7d808f0014504.svg
paper_authors.fullname: Zhilong Wang
paper_authors.type: user
paper_authors.user: bufan
paper_authors._id: 682fe3a565bac3ec3556fc77
paper_authors.name: Jinyao Liu
paper_authors._id: 682fe3a565bac3ec3556fc78
paper_authors.name: Runmin Ma
paper_authors._id: 682fe3a565bac3ec3556fc79
paper_authors.name: Tianshuo Peng
paper_authors._id: 682fe3a565bac3ec3556fc7a
paper_authors.name: Peng Ye
paper_authors._id: 682fe3a565bac3ec3556fc7b
paper_authors.name: Dongzhan Zhou
paper_authors._id: 682fe3a565bac3ec3556fc7c
paper_authors.name: Shufei Zhang
paper_authors._id: 682fe3a565bac3ec3556fc7d
paper_authors.name: Xiaosong Wang
paper_authors._id: 682fe3a565bac3ec3556fc7e
paper_authors.name: Yilan Zhang
paper_authors._id: 682fe3a565bac3ec3556fc7f
paper_authors.name: Meng Li
paper_authors._id: 682fe3a565bac3ec3556fc80
paper_authors.name: Zhongying Tu
paper_authors._id: 682fe3a565bac3ec3556fc81
paper_authors.name: Xiangyu Yue
paper_authors._id: 682fe3a565bac3ec3556fc82
paper_authors.name: Wangli Ouyang
paper_authors._id: 682fe3a565bac3ec3556fc83
paper_authors.name: Bowen Zhou
paper_authors._id: 682fe3a565bac3ec3556fc84
paper_authors.name: Lei Bai
paper_title: NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop
    System from Hypothesis to Verification
paper_summary: 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.