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

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May 19, 2026 Science Co-Scientist: A multi-agent AI partner to accelerate research Co-Scientist team

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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.

Generated ideas are iteratively refined, critiqued and evolved into new hypotheses, forming a virtuous cycle of scientific reasoning and hypothesis generation.

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 .

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The idea tournament is iteratively ranking hypotheses via an Elo-based tournament while also injecting fresh knowledge to expand its exploration of the hypothesis space.

Validating Co-Scientist in the lab, starting with life sciences Over the past year, we have collaborated with global experts to evaluate Co-Scientist on complex problems in the life sciences. We have also been previewing an enterprise-grade version with a number of organizations including Daiichi Sankyo, Bayer Crop Science, and the US National Laboratories as part of the Genesis Mission .

Uncovering repurposed medicines to fight liver fibrosis Co-Scientist helped accelerate Gary Peltz’s search for liver fibrosis treatments. The AI system highlighted overlooked drug-repurposing candidates, including one that successfully blocked 91% of a scarring-linked response in lab tests. The results, published in Advanced Science, point toward new gene-regulating approaches to treat chronic liver disease. “ Co-Scientist feels like a collaborator that’s read everything available about biomedical science, with the reasoning capabilities to find the connections that we’re currently missing. ” Professor Gary Peltz, Stanford University School of Medicine

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