title: Scaling Laws in Scientific Discovery with AI and Robot Scientists
summary: Scientific discovery is poised for rapid advancement through advanced
  robotics and artificial intelligence. Current scientific practices face
  substantial limitations as manual experimentation remains time-consuming and
  resource-intensive, while multidisciplinary research demands knowledge
  integration beyond individual researchers' expertise boundaries. Here, we
  envision an autonomous generalist scientist (AGS) concept combines agentic AI
  and embodied robotics to automate the entire research lifecycle. This system
  could dynamically interact with both physical and virtual environments while
  facilitating the integration of knowledge across diverse scientific
  disciplines. By deploying these technologies throughout every research stage --
  spanning literature review, hypothesis generation, experimentation, and
  manuscript writing -- and incorporating internal reflection alongside external
  feedback, this system aims to significantly reduce the time and resources
  needed for scientific discovery. Building on the evolution from virtual AI
  scientists to versatile generalist AI-based robot scientists, AGS promises
  groundbreaking potential. As these autonomous systems become increasingly
  integrated into the research process, we hypothesize that scientific discovery
  might adhere to new scaling laws, potentially shaped by the number and
  capabilities of these autonomous systems, offering novel perspectives on how
  knowledge is generated and evolves. The adaptability of embodied robots to
  extreme environments, paired with the flywheel effect of accumulating
  scientific knowledge, holds the promise of continually pushing beyond both
  physical and intellectual frontiers.
paper_authors._id: 67ef33a4456bcf30fa95b2f1
paper_authors.name: Pengsong Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-04T07:09:42.152Z
paper_authors._id: 655fb8a122ce47e5fa491c72
paper_authors.avatarUrl: /avatars/d320fe777649b68fbd1372865a2f4def.svg
paper_authors.fullname: Pengsong Zhang
paper_authors.type: user
paper_authors.user: universea
paper_authors._id: 67ef33a4456bcf30fa95b2f2
paper_authors.name: Heng Zhang
paper_authors._id: 67ef33a4456bcf30fa95b2f3
paper_authors.name: Huazhe Xu
paper_authors._id: 67ef33a4456bcf30fa95b2f4
paper_authors.name: Renjun Xu
paper_authors._id: 67ef33a4456bcf30fa95b2f5
paper_authors.name: Zhenting Wang
paper_authors._id: 67ef33a4456bcf30fa95b2f6
paper_authors.name: Cong Wang
paper_authors._id: 67ef33a4456bcf30fa95b2f7
paper_authors.name: Animesh Garg
paper_authors._id: 67ef33a4456bcf30fa95b2f8
paper_authors.name: Zhibin Li
paper_authors._id: 67ef33a4456bcf30fa95b2f9
paper_authors.name: Arash Ajoudani
paper_authors._id: 67ef33a4456bcf30fa95b2fa
paper_authors.name: Xinyu Liu
paper_title: Scaling Laws in Scientific Discovery with AI and Robot Scientists
paper_summary: Scientific discovery is poised for rapid advancement through advanced
  robotics and artificial intelligence. Current scientific practices face
  substantial limitations as manual experimentation remains time-consuming and
  resource-intensive, while multidisciplinary research demands knowledge
  integration beyond individual researchers' expertise boundaries. Here, we
  envision an autonomous generalist scientist (AGS) concept combines agentic AI
  and embodied robotics to automate the entire research lifecycle. This system
  could dynamically interact with both physical and virtual environments while
  facilitating the integration of knowledge across diverse scientific
  disciplines. By deploying these technologies throughout every research stage --
  spanning literature review, hypothesis generation, experimentation, and
  manuscript writing -- and incorporating internal reflection alongside external
  feedback, this system aims to significantly reduce the time and resources
  needed for scientific discovery. Building on the evolution from virtual AI
  scientists to versatile generalist AI-based robot scientists, AGS promises
  groundbreaking potential. As these autonomous systems become increasingly
  integrated into the research process, we hypothesize that scientific discovery
  might adhere to new scaling laws, potentially shaped by the number and
  capabilities of these autonomous systems, offering novel perspectives on how
  knowledge is generated and evolves. The adaptability of embodied robots to
  extreme environments, paired with the flywheel effect of accumulating
  scientific knowledge, holds the promise of continually pushing beyond both
  physical and intellectual frontiers.
paper_ai_summary: An autonomous generalist scientist combines AI and robotics to automate the research lifecycle, integrating knowledge across disciplines and reducing time and resources for scientific discovery.
paper_ai_keywords: autonomous generalist scientist agentic AI embodied robotics research lifecycle literature review hypothesis generation experimentation manuscript writing virtual AI scientists robot scientists