Meet AI-CO scientist: a multi-agent system driven by Gemini 2.0 for accelerating scientific discovery

Biomedical researchers face a significant dilemma in their quest for scientific breakthroughs. The increasing complexity of biomedical issues requires deep, specialized expertise, while transformative insights often arise at the intersection of different disciplines. This tension between depth and width creates significant challenges for researchers navigating in an exponentially growing amount of publications and specialized high capacity technologies. Despite these obstacles, the great scientific progress often stems from trans-disciplinary approaches, including the development of CRISPR that exemplifies this pattern that combines techniques from microbiology, genetics and molecular biology. Such examples highlight how crossing traditional boundaries can catalyze scientific progress, even when scientists struggle to maintain specialized knowledge and interdisciplinary awareness.

Recent approaches focus on developing specialized “reasoning models” that try to excel human thought processes rather than simply predict the next words. The test-time calculation paradigm has emerged as a promising direction that assigns additional calculation resources under inference to enable deliberate reasoning. This concept evolved from early successes such as Alphagos Monte Carlo Tree search and has been expanded to LLMS. At the same time, AI has transformed scientific discovery across domains, exemplified by Alphafold 2’s breakthrough in protein structure prediction. Researchers now aim to complete the integration of AI into the research process and want to establish AI as an active partner throughout the scientific process, from hypothesis generation to screenwriting.

In addition, various AI systems have emerged to accelerate scientific discovery in biomedical research. Koscientist, a GPT-4-powered Multi-Agent system, enables autonomous performance of chemical experiments through integrated web search and code execution functions. In addition, both general models such as GPT-4 and specialized biomedical LLMs such as MED-Palm have shown impressive benefits on biomedical reasoning benchmarks. In the case of drugs recycled specifically, traditional approaches combine calculation and experimental methods using disease drug interaction understanding. Knowledge graph -based methods such as graphic intervention networks and txgnn show promise, but remains limited by knowledge graph quality, scalability problems and inadequate explainability.

Researchers from Google Cloud AI Research, Google Research, Google Deepmind, Houston Methodist, Sequomome, Fleming Initiative and Imperial College London and Stanford University School of Medicine have proposed an AI-CO scientist, a multi-agent system built on Gemini 2.0 designed to speed up science discovery. It aims to reveal new knowledge and generate new research hypotheses that are adapted to scientifically delivered goals. Using a “generation, debate and evolving” approach, the AI-CO-scientist uses test time compute scaling to improve the generation of hypothesis. In addition, it focuses on three biomedical domains: drugs recycled, new target discovery and explanation of bacterial development mechanisms. Automatic evaluations show that increased test time calculation consistently improves hypothesis quality.

AI-CO-Scientific Architecture integrates four essential components and forms an extensive research system:

• The natural language interface allows researchers to interact with the system, define research goals, give feedback and guide progress through conversation inputs.
• The asynchronous task framework implements a multi-agent system where specialized agents act as workers processes within a continuous execution environment.
• A supervisor agent orchestrates the above framework by managing the worker task queue, assigning specialized agents to processes and assigning calculation resources.
• To enable iterative calculation and scientific reasoning over long time horizons, co-scientists use a sustained context memory to store and pick up states for the agents and system during the calculation.

At the core of the AI-CO-scientific system lies a coalition of specialized agents orchestrated by a supervisor agent. There are several types of specialized agents. From the generation agent, it initiates research by creating the original focus areas and hypotheses. Furthermore, the reflection agent acts as a peer reviewer who critically examines hypothesis quality, correctness and news. The ranking agent implements an ELO-based tournament system with paired comparisons to assess and prioritize hypotheses. The proximity agent calculates equality graphs for hypothesis clusters, deduplication and effective exploration of conceptual landscapes. Evolution Agent fine-tuning continuously top-ranked hypotheses. Finally, the meta-review agent synthesizes insight from all reviews and tournament debates to optimize agent’s performance in subsequent iterations.

AI-CO-Scientific System shows strong results across multiple evaluation measurements. Analysis using GPQA Diamond SET shows the agreement between ELO rates and accuracy, with the system achieving 78.4% top-1 accuracy by choosing its highest ranked result for each question. In addition, newer reasoning models such as Openai O3-Mini-High and Deepseek R1 show competition performance with less computing, while the co-scientist shows no evidence of performance saturation, suggesting that further scaling could provide further improvements. Expert valuations across 11 research goals confirm the Co-scientist’s efficiency, where output receives the highest preference cranging (2.36/5) and superior news (3.64/5) and influence (3.09/5) assessments compared to baseline models.

Further results with AI-C-CO-scientists show significant capabilities across multiple biomedical research domains. In liver fibrosis research, the system generates when it is tasked with exploring epigenetic changes, 15 hypotheses. These hypotheses identify 3 new epigenetic modifiers as potential therapeutic goals, supported by preclinical evidence. Subsequent testing in liver organoids confirms that drugs targeting two of these modifiers exhibit anti-fibrotic activity without cellular toxicity. In particular, an identified compound is already FDA approved for another indication that presents an immediate medicament that is recycled for liver fibrosis treatment. In antimicrobial resistance research, the co-scientist precisely suggests examining Kapsid-tail interactions and is precisely adjusted with the researchers’ discovery that CF-Picis interacts with different professional tails to expand their host area.

This research document also provides the restriction that AI-CO-Scientific System faces:

• Restrictions on literature search, reviews and reasoning.
• Lack of access to data on negative results.
• Improved multimodal reasoning and tool use features.
• Inherited restrictions of Frontier llms.
• Need for better measurements and broader evaluations.
• Limitations of existing validations.

The AI-CO scientist is currently not designed to generate extensive clinical experimental designs or to fully explain factors such as drug bioteliability, pharmacokinetics and all complex drug interactions when used for drugs recycled or detected.

AI-CO-Scientific System provides several options for Future development across multiple fronts. Immediate improvements should focus on improving literature reviews, implementing external tool control, strengthening factuality verification and improving the citation house to tackle unanswered research. Coherence control will also reduce the burden by undergoing defective hypotheses. A greater progress would involve expansion in addition to text analysis to incorporate images, data sets and larger public databases. Finally, integration with laboratory automation systems could create closed loop-validation cycles, while more structured user interfaces could improve human-ai collaborative efficiency beyond current free-text interactions.

IN ConclusionResearchers introduced AI-CO scientist, a multi-agent system to accelerate scientific discovery through agent AI systems. Using its “generate, debate, developing” methodology with specialized agents working together, the system shows a remarkable potential to increase human scientific endeavors. The experimental validation across multiple biomedical domains confirms its ability to generate new, testable hypotheses that can withstand the real world. As researchers are facing increasingly complex challenges in human health, medicine and broader scientific domains, systems such as AI-CO-scientists offer meaningful acceleration of discovery processes. This human-focused AI development creates new opportunities to help people solve major scientific challenges effectively.

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Sajjad Ansari is a last year bachelor from IIT KHARAGPUR. As a technical enthusiast, he covers the practical uses of AI focusing on understanding the impact of AI technologies and their real world. He aims to formulate complex AI concepts in a clear and accessible way.