Democratizing AI scientists using ToolUniverse

• URL: http://arxiv.org/abs/2509.23426v2
• Date: Wed, 22 Oct 2025 01:03:00 GMT
• Title: Democratizing AI scientists using ToolUniverse
• Authors: Shanghua Gao, Richard Zhu, Pengwei Sui, Zhenglun Kong, Sufian Aldogom, Yepeng Huang, Ayush Noori, Reza Shamji, Krishna Parvataneni, Theodoros Tsiligkaridis, Marinka Zitnik,
• Abstract summary: In genomics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development.<n>We present ToolUniverse, an ecosystem for building AI scientists from any language or reasoning model across open- and closed-weight models.
• Score: 32.32301676392716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid workflows, and lack shared environments that unify tools, data, and analyses into a common ecosystem. In genomics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development; AI scientists require comparable infrastructure. We present ToolUniverse, an ecosystem for building AI scientists from any language or reasoning model across open- and closed-weight models. ToolUniverse standardizes how AI scientists identify and call tools by providing more than 600 machine learning models, datasets, APIs, and scientific packages for data analysis, knowledge retrieval, and experimental design. It automatically refines tool interfaces for correct use by AI scientists, generates new tools from natural language descriptions, iteratively optimizes tool specifications, and composes tools into agentic workflows. In a case study of hypercholesterolemia, ToolUniverse was used to create an AI scientist to identify a potent analog of a drug with favorable predicted properties. The open-source ToolUniverse is available at https://aiscientist.tools.

Related papers

• Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale [82.20980951765891]
  We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated Bohrium+SciMaster.<n>Bohrium acts as a managed, traceable hub for AI4S assets that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities.<n>SciMaster orchestrates these capabilities into long-horizon scientific, on which scientific agents can be composed and executed.
  arXiv  Detail & Related papers  (2025-12-23T16:04:41Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration [39.43814195462455]
  SciToolAgent automates hundreds of scientific tools across biology, chemistry, and materials science.<n>The agent also incorporates a comprehensive safety-checking module to ensure responsible and ethical tool usage.
  arXiv  Detail & Related papers  (2025-07-27T13:55:35Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• The AI Cosmologist I: An Agentic System for Automated Data Analysis [0.0]
  The AI Cosmologist implements a complete pipeline from idea generation to experimental evaluation and research dissemination.<n>Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies.<n>Results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery.
  arXiv  Detail & Related papers  (2025-04-04T13:12:08Z)
• Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
  An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
  arXiv  Detail & Related papers  (2025-03-28T14:00:27Z)
• VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities [0.19736111241221438]
  generative AI presents an opportunity to bridge this knowledge gap.<n>We present a modular architecture for the Virtual Scientific Companion (VISION)<n>With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an X-ray scattering beamline.
  arXiv  Detail & Related papers  (2024-12-24T04:37:07Z)
• Empowering Biomedical Discovery with AI Agents [15.125735219811268]
  We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
  arXiv  Detail & Related papers  (2024-04-03T16:08:01Z)
• AI and Generative AI for Research Discovery and Summarization [3.8601741392210434]
  AI and generative AI tools have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. One area that these tools can make a substantial impact is in research discovery and summarization. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future.
  arXiv  Detail & Related papers  (2024-01-08T18:42:55Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.