Accelerating scientific discovery with Co-Scientist

Key points

• Problem: As publications grow quickly, scientists struggle to combine deep subject expertise with broad knowledge that bridges disciplines. Literature summarization and deep research tools already synthesize existing information, so the authors target a system that proposes novel hypotheses for experimental validation.
• Input and output: This is an implemented system paper. It takes a research goal written in natural language (from short statements to documents of tens of thousands of tokens and hundreds of paper PDFs) with preferences and constraints, and returns a ranked list of hypotheses and experiment plans plus a research overview written by the Meta-review agent. Scientists can refine the goal during a run and add their own hypotheses and reviews to the tournament.
• Method: A Supervisor agent turns the goal into a research plan configuration and asynchronously assigns, from a task queue, the Generation (hypothesis generation), Reflection (peer-review style critique), Ranking, Proximity (clustering of similar hypotheses), Evolution (refinement of top hypotheses), and Meta-review (synthesis of reviews) agents. The Ranking agent starts each new hypothesis at an Elo rating of 1200 and runs a pairwise tournament, comparing top-ranked hypotheses through multi-turn scientific debates and lower-ranked ones through single-turn comparisons. New hypotheses from the Evolution agent compete again, and the Meta-review agent collects recurring issues from reviews and debates and appends them to the agents' prompts in the next iteration, so feedback propagates without fine-tuning.
• Implementation: All agents use Gemini 2.0 models as the base model, with web search, open databases, a scientist-specified repository of papers and experimental data, and specialized models such as AlphaFold as tools. The full source code is not public; the authors publish pseudocode (Supplementary Note 8) and the exact agent prompts (Supplementary Note 9).
• Results: In a blinded expert evaluation on 11 curated goals, Co-Scientist was the most preferred with an average preference rank of 2.36 (1 most preferred, 4 least), and its novelty and impact ratings of 3.64 and 3.09 out of 5 were higher than those of three baselines, Gemini 2.0 Flash Thinking Experimental, Gemini 2.0 Pro Experimental, and OpenAI o1. In the ablation, the Reflection agent without search gave already published ideas a novelty score of 6.14 out of 10, which fell to 2.38 with search. In the wet lab, the new candidate drug KIRA6 had an IC50 (the drug concentration that halves cell viability) of 10 nM in KG-1a leukemia cells and 180 nM in the non-leukemic control line TK6, an 18-fold separation.
• Limits: The authors state that Co-Scientist relies on open-access literature, so it can miss paywalled prior art and negative results, and that it inherits the hallucination risk of its base models. They also note that Elo is a self-assessed score without independent ground truth, the expert evaluations are small in scale, and the cell line experiments are an initial biological validation step.

Paper links

External research summaries. These are not HDATF publications or measured product results.