Co-Scientist: A multi-agent AI partner to accelerate research

May 19, 2026 Science

Co-Scientist team

Introducing a collaborative AI partner for researchers to develop new hypotheses in life sciences and beyond.

Every great scientific breakthrough begins with a single, transformative idea. The spark of discovery relies on a researcher's ability to connect disparate facts and formulate the right hypothesis to test. But in an era of information overload and increasingly complex challenges, the search for these needle-in-a-haystack ideas has become a significant bottleneck for progress.

We believe AI can help dramatically accelerate the pace of breakthroughs by serving as a dedicated partner in the generation and refinement of breakthrough scientific hypotheses.

Today, in Nature we published our latest Co-Scientist research, introducing a new multi-agent AI system built with Gemini that iteratively generates, debates, and evolves novel hypotheses for complex scientific problems.

We are making the Co-Scientist system available to individual researchers through Hypothesis Generation, a new experimental tool jointly developed across Google DeepMind, Google Research, Google Cloud and Google Labs. We’ll begin rolling out in the coming weeks and researchers can register their interest at labs.google/science.

Since sharing our early research last year, we’ve been developing and testing Co-Scientist together with teams who are leveraging it to tackle challenging problems - from antimicrobial resistance and plant immunity to liver fibrosis. We’re excited to share some of the ways it is already being applied across fundamental biology, the natural sciences, and engineering.

How Co-Scientist works: A multi-agent system built with Gemini

Scientific discovery is rarely a straight line; it is a cycle of ideation and hypothesis generation, critique, and refinement. Scientists often reach their most profound insights only after wrestling with a complex problem for days, months, or even years. The core research question behind Co-Scientist was: How can an AI system engage in this rigorous structured thinking for scientific discovery?

The Co-Scientist AI system is made of a collaborative coalition of specialized agents based on the Gemini model, which we can group into three different phases:

Generate ideas:

• Generation agent - Proposes initial focus areas and novel hypotheses grounded in scientific literature and data.
• Proximity agent - Maps and clusters generated hypotheses to help ensure a diverse, comprehensive exploration of the research space.

Debate ideas:

• Reflection agent - Acts as a "virtual peer reviewer," critically evaluating hypotheses for correctness, quality, and novelty.
• Ranking agent - Orchestrates an “idea tournament”, using pairwise comparisons and simulated scientific debates to prioritize the most promising paths and hypotheses.

Evolve ideas:

• Evolution agent - Continuously refines, combines, and builds upon the top-ranked hypotheses in the tournament to help iteratively improve their quality.
• Meta-review agent - Synthesizes insights from the debates and idea tournament to continuously optimize the system and generates the final research proposal for the scientist to review.

Orchestrating the agent coalition is a supervisor agent acting as an adaptive planner. Unlike AI models that think linearly, this freeform planner breaks down high-level research goals into executable steps, coordinating agents to run in parallel and explore multiple avenues simultaneously.

Tournament of ideas: How our system verifies, refines, and ranks hypotheses

Co-Scientist can explore thousands of research directions. To help find the most impactful ones, we developed the ‘tournament of ideas’. The approach draws from principles used in AlphaGo and AlphaStar - but instead of playing a game, our AI agents hold scientific debates to generate, refine and rank ideas.

To push the boundaries of novelty while ensuring the hypotheses are robust and testable, the majority of the system's computation is dedicated to verifying these hypotheses. By deeply cross-checking claims against scientific literature and data, the system ensures that claims remain grounded, factually accurate, and logically coherent. The system currently integrates web search and specialized databases like ChEMBL and UniProt to incorporate additional knowledge. It can also leverage advanced specialized models as tools like AlphaFold, which we are testing in select research collaborations.

This combination of these capabilities helps make Co-Scientist one of the first examples of a reliable multi-agent system for structured scientific thinking, enabling it to deliver tangible results in novel hypothesis generation for complex scientific problems .

Developing agentic tools with the scientific community

Co-Scientist was developed in collaboration with researchers from over 100 institutions to test its capabilities and ensure it is a high-quality, useful tool for the scientific community.

As part of our responsible AI approach, Co-Scientist underwent extensive internal and external safety evaluations. Given Co-Scientist’s demonstrated proficiency in life and physical sciences, we also conducted independent evaluations for misuse in Chemical, Biological, Radiological and Nuclear (CBRN) domains. From these findings, we developed custom safety classifiers to flag unethical research goals and mitigate the surfacing of unsafe information.

We will continue to iterate and develop the tool alongside feedback and collaboration with the scientific community and are excited to be making Co-Scientist available to individual researchers through Gemini for Science. We also look forward to expanding access to more Google Cloud enterprise partners soon.

We have been deeply inspired by the scientists who have built up our understanding of the world today. And we hope that AI can help researchers to usher in and accelerate a new era of scientific progress.

Note: Co-Scientist is intended to be a partner in research, not a replacement for scientific or clinical expertise, and users are responsible for any decisions they make using the outputs as they continue their scientific journey.

Acknowledgements

This research project was led by Juraj Gottweis and Vivek Natarajan, as well as Alan Karthikesalingam, Annalisa Pawlosky, and Yunhan Xu, and with key contributions from: Wei-Hung Weng, Alexander Daryin, Alessio Orlandi, Andrew Carroll, Anil Palepu, Artiom Myaskovsky, Avinatan Hassidim, Ben Feinstein, Burak Gokturk, Byron Lee, Dan Popovici, Dina Zverinski, Eeshit Dhaval Vaishnav, Elahe Vedadi, Fan Zhang, Felix Weissenberger, Florian Hasler, Gary Peltz, Grzegorz Glowaty, Ivor Rendulic, Ivan Budiselic, Jacob Blum, Jan Freyberg, Jeremy Ratcliff, José R Penadés, Katherine Chou, Kavita Kulkarni, Keran Rong, Khaled Saab, Luka Rimanic, Marina Boia, Matthias Bellaiche, Nenad Tomašev, Ottavia Bertolli, Petar Sirkovic, Ryutaro Tanno, Tao Tu, Tiago R D Costa, Tom Sheffer, Vikram Dhillon, Yuan Guan, Amin Vahdat, James Manyika, Demis Hassabis, Yossi Matias and Pushmeet Kohli.

We thank our teammates Ali-Cowen Rivers, Anna Trostanetski, Barnaby James, Bill Byrne, Boon Panichprecha, Charlie Taylor, Diego Ballesteros, Hussein Hassan Harrirou, Ieva Grublyte, Ivan Lee, Jakob Oesignhaus, James Walker, Jorge Barrios, Laurynas Tamulevičius, Luka Važić, Meet Shah, Mihai Ciorobea, Natasha Latysheva, Nicolas Stroppa, Nir Kerem, Saz Basu, Sebastian Nowozin, Taylor Applebaum, Team Rakket and, Thomas Wagner and Yaniv Carmel for their technical support.

We also want to thank Carmela Sidrauski, Clare Bryant, Filippo Menolascina, Jonathan Gootenberg, Katherine Labbé, Matthew Onsum, Omar Abudayyeh, Ritu Raman, Ryan Flynn, Velia Siciliano for their collaboration.

Finally, we thank Ali Eslami, Andy Berndt, Ankur Jain, Anna Koivuniemi, Clemens Mayer, Dale Webster, Greg Corrado, Jason Freidenfelds, Jeff Dean, Joelle Barral, John Jumper, John Platt, Josh Woodward, Karen DeSalvo, Koray Kavukcuoglu, Michael Brenner, Michael Howell, Noam Shazeer, Oriol Vinyals, Parthasarathy Ranganathan, Ronit Levavi Morad, Royal Hansen, Scott Huffman, Srini Narayanan, Susan Thomas, Thomas Kurian, Zoubin Ghahramani and Sundar Pichai for their support of this work.