Accelerating scientific discovery with Co-Scientist

In plain words

Scientists spend a lot of effort deciding which questions are worth testing in the lab. Co-Scientist is a software system, built on Google's Gemini language models, that tries to help with that first step. A scientist describes a research goal in plain language. A group of AI "agents" then drafts possible research hypotheses, checks them against published work, and makes them compete. Pairs of ideas are compared in a rating tournament, similar to chess ratings, and the strongest ideas are reworked and entered again.

The core idea is to spend more computing time on thinking. In the authors' tests, the longer the system ran, the better its top ideas scored under an automatic rating. The authors also took a few of its suggestions into real lab experiments on three biomedical problems: drug repurposing for a blood cancer, a possible target for liver scarring, and how a bacterial mechanism works. Human experts stayed in the loop throughout, setting goals and reviewing output.

It matters because it shows one way AI might speed up the early, choose-what-to-test stage of research. The evaluations are small, and the authors say so. The system's source code is not public, so its claims cannot be checked independently.

TL;DR

Co-Scientist is a multi-agent system on Gemini 2.0 that generates, reviews, ranks in an Elo tournament and evolves research hypotheses, using more test-time compute to improve them p. 6, p. 29. The authors report that the Elo rating of top hypotheses rises steadily as the system runs fig. 2a, experts prefer its output over several LLM baselines on 11 goals fig. 2c, and some suggestions held up in cell-line assays for acute myeloid leukemia (AML) and liver fibrosis p. 13–19. The source code is not public and the evaluations are small, so treat this as a description of an architecture, not a reproducible baseline.

Problem

Scientists face a breadth-and-depth problem: useful insight can come from distant fields, but no one can master every field p. 2. The authors also argue that test-time compute, and the reasoning and multimodal abilities of current models, make a general-purpose hypothesis generator feasible p. 2–3. Drug development is slow and expensive, and drug repurposing is a pragmatic way to shorten it p. 3, p. 11. Single-pass LLM outputs can be plausible but wrong or not novel, and the authors note imperfect factuality and hallucination as limits p. 7, p. 24. The task the paper targets is to generate and rank research hypotheses that a scientist can test in the lab, with the scientist steering the process p. 3, p. 36–37.

Method

The paper has no section numbers. [p. N] cites the page of the arXiv v2 PDF we read (page 1 is the title page). Figure and table numbers are as printed.

Architecture. The scientist's research goal is the entry point, and it may include constraints, preferences and documents p. 30. A Supervisor agent turns the goal into a research plan configuration, which sets the preferences, the evaluation criteria and whether only novel hypotheses are wanted p. 30. The default criteria are alignment with the goal, plausibility, novelty, testability and safety p. 29. The Supervisor puts tasks on an asynchronous queue, assigns them to worker processes, and weights and samples the agent types p. 29, p. 31. It also periodically writes summary statistics to a persistent context memory, which is used to decide on allocation, to decide when the run has reached a terminal state, and to restart after failures p. 29–31.

Agents. Each specialized agent is a set of prompts ("library of strategies") that may call external tools, such as web search and specialized AI models, through APIs p. 30. Six agent types are described.

1. Generation builds an initial list of hypotheses through literature exploration with web search, simulated scientific debates, identification of testable intermediate assumptions, and research expansion guided by the previous meta-review p. 32.
2. Reflection reviews hypotheses in stages. An initial review without tools discards flawed, non-novel or unsafe hypotheses. Passing hypotheses get a full review with web search covering correctness, quality and novelty. Other review types are deep verification (split the hypothesis into assumptions, check each one, and judge whether a wrong assumption is fundamental), observation review (does the hypothesis explain existing findings better), simulation review (step through the proposed experiment to find failure cases), and tournament-aware review p. 33–34. The order and trigger rules for the non-initial review types are not stated.
3. Ranking runs an Elo tournament. New hypotheses start at 1200 p. 34. Top ranked hypotheses are compared in multi-turn scientific debates, and lower ranked ones in a single-turn pairwise comparison p. 34.
4. Proximity computes a similarity graph over hypotheses, relative to the goal. It is used for clustering, de-duplication and for choosing matches p. 35.
5. Evolution refines top-ranked hypotheses by grounding them in literature, improving coherence and feasibility, drawing inspiration from other hypotheses, combining them, simplifying them, and exploring divergent ideas. It creates new hypotheses rather than replacing existing ones, and each new hypothesis must win its place in the tournament p. 35.
6. Meta-review summarizes reviews and tournament debates into a critique. Its feedback is appended to every agent's prompt in the next iteration, so the system improves without fine-tuning or back-propagation p. 31, p. 35–36. At the end it writes a research overview with suggested experiments and suggested domain experts. It can use constrained decoding to follow formats such as NIH Specific Aims p. 36.

Tournament. Each pairing runs a debate or a comparison. The authors say debates reduce ordering bias and focus on novelty, correctness and testability p. 34. Pairings are chosen by proximity, and newer or top-ranked hypotheses are prioritized p. 35. Ratings are Elo-style. The paper gives no update formula and no K-factor.

Scaling test-time compute. The authors measure hypothesis quality as the run goes on. They split the generated hypotheses into ten time buckets, each covering a tenth of the generation time, and report the best Elo and the top-10 average Elo per bucket, averaged over 203 goals p. 7.

Expert in the loop. Scientists define the goal, refine it after seeing output, review hypotheses, add their own hypotheses to the tournament, and make the final selection from the ranked list and overview p. 36–37. The authors report under one hour of expert time to write the AML prompt and about three hours for the final review p. 36–37.

Ablations (author-reported). Removing search from Reflection lets the system score a known non-novel idea as more novel (6.14 out of 10 without search, 2.38 with search) p. 38. The debate prompt for Ranking reduces positional bias p. 7, p. 38. The full ablation tables are in the supplement, which is not in the PDF we read p. 38.

Key equations: none in the main text. The Elo update is not written out.

Pseudocode. The loop below is our summary of the controller described in the text.

# Supervisor loop over an asynchronous worker queue  [p. 29, p. 31]
# Input:  research_goal (text), preferences (text)
# Output: ranked hypotheses and a research overview

def run_co_scientist(research_goal, preferences):
    # Turn the goal into a research plan configuration  [p. 30]
    plan = parse_research_plan(research_goal, preferences)
    # Persistent context memory shared across iterations  [p. 29]
    memory = load_or_create_context_memory()
    while not reached_terminal_state(memory, plan):
        # Weight and sample agent types for the next tasks; the weighting
        # scheme is not stated  [p. 31]
        tasks = sample_agent_tasks(memory, plan)
        for task in tasks:
            # Workers run generation, reflection, ranking, proximity,
            # evolution or meta-review  [p. 30–31]
            result = run_worker(task, memory)
            memory.store(result)
        # Meta-review feedback goes into every agent prompt next round,
        # without fine-tuning  [p. 31]
        memory.set_feedback(meta_review(memory))
        # Periodic statistics guide what runs next  [p. 29]
        memory.update_statistics()
    # Final ranked list and research overview  [p. 36]
    return research_overview(memory)

# Elo tournament over hypotheses  [p. 34–35]
# Input:  hypotheses, their ratings (new ones start at 1200)
# Output: updated ratings

def run_tournament(hypotheses, ratings, proximity_graph):
    # Choose pairs: similar hypotheses first, and newer or top-ranked
    # ones more often  [p. 35]
    pairs = pick_pairs(hypotheses, proximity_graph)
    for first, second in pairs:
        # Top-ranked pairs get a multi-turn debate; the number of turns
        # is not stated  [p. 34]
        if is_top_ranked(first) and is_top_ranked(second):
            winner = multi_turn_debate(first, second)
        # Lower-ranked pairs get one comparison turn  [p. 34]
        else:
            winner = single_turn_comparison(first, second)
        # Update formula and K-factor are not stated  [p. 34]
        ratings = elo_update(ratings, winner, first, second)
    return ratings

# Reflection, first two stages for one hypothesis  [p. 33]
# Input:  hypothesis (text)
# Output: review record, or None if discarded

def review_hypothesis(hypothesis):
    # Initial review without external tools: flawed, non-novel or
    # unsafe hypotheses are dropped here  [p. 33]
    if not passes_initial_review(hypothesis):
        return None
    # Full review with web search for correctness, quality and novelty  [p. 33]
    return full_review_with_search(hypothesis)

Hyperparameters

Few numeric settings are given in the main text. The supplement, which holds the prompts and pseudocode, is not in the PDF we read.

Results

Author-reported. The setup is given in each row. Plot values are read by eye from the figures, and the paper gives no table for them.

The authors call the LLM-judge and expert results preliminary and small in scale p. 11.

AML single agents, MOLM-13 unless noted (IC50, µM, 95% CI, as printed in the figures):

The paper reports KIRA6 as 18-fold more potent in KG-1a than in TK6 fig. 3 p. 15. The text gives 10 nM for KG-1a. See Pitfalls for the conflict between the point estimate and its own CI.

Single-agent IC50 table (MOLM-13 and KG-1a, µM):

Source: [ext. tab. 1]. The caption defines "no response" as not reaching 50% growth inhibition within the tested range.

Drug combinations (interaction call per cell line, as printed):

The experts selected the top three novel single-agent candidates for testing: Nanvuranlat, KIRA6 and Leflunomide p. 15. KIRA6 showed inhibition in several cell lines p. 15. Nanvuranlat and Leflunomide showed limited effect on MOLM-13 [ext. fig. 4] p. 16; the paper does not report them in other lines.

Other real-world validations (author-reported, no numbers given in the text):

• Liver fibrosis: three predicted epigenetic modifiers, two of which showed anti-fibrotic activity in human hepatic organoids without cellular toxicity. One effective drug, vorinostat, is FDA-approved for another cancer p. 19.
• Antimicrobial resistance: the system independently proposed a mechanism for how capsid-forming phage-inducible chromosomal islands spread across hosts, matching a co-timed unpublished discovery. The authors report this took two days p. 19 p. 24 fig. 1c.

Paper vs code

No official code.

What I searched: (1) the paper and its arXiv comments, which link no code; the paper's "Code availability" section says the full source code is not publicly available p. 40; (2) no project page is linked; (3) a GitHub repository search for "co-scientist google" returned 15 repositories, all third-party reimplementations (for example Kaimen-Inc/Co-Scientist, jataware/open-coscientist, llnl/open-ai-co-scientist). None belongs to the authors or to Google. Per the rules, I did not use them.

The supplement's pseudocode (Supplementary Note 8) and prompts (Supplementary Note 9) are referenced but are not in the PDF we read, so the pseudocode above is our summary of the main text, not a checked implementation.

Pitfalls

• Not reproducible from the paper. The source is not released p. 40. The pseudocode and prompts are in a supplement that is not in the PDF we read p. 40, p. 43.
• Missing details. Not stated: the Elo K-factor and update rule; the number of debate turns; what "top-ranked" means for the debate-versus-comparison choice; the similarity measure in the Proximity agent; the Supervisor's weighting and sampling rule; the number of workers; the tournament size; the compute budget per run; the stopping rule; the context-memory format; the Gemini version used for the main runs p. 29–36.
• Elo is self-evaluation. The Elo ratings come from the system's own LLM judgements, not from ground truth [ext. fig. 1] p. 10. The authors say Elo is not the optimization target p. 8. New hypotheses enter at 1200 p. 34, so later time buckets mix hypotheses with different match histories. The paper does not discuss how this affects bucket comparisons.
• Binimetinib claim conflicts with the figures. The text says IC50 is "as low as 2 nM in all AML cell lines (except NOMO-1)" p. 13. Fig. 3a gives 0.01 µM for MOLM-13 and ext. fig. 3a gives 0.02 µM for KG-1a. The 2 nM value matches only HL-60 [ext. fig. 3b]. Use the figure values.
• KIRA6 point estimate outside its CI. In fig. 3e the KG-1a IC50 is 0.01 µM, but its 95% CI is 0.012 to 0.081 µM. The text gives 10 nM p. 15. Treat the KG-1a value as uncertain.
• Cell-line count. The statistics section says "five distinct AML cell lines" p. 38. The validation section lists four AML lines plus TK6 as a non-AML control p. 13. The "five" includes the control.
• "Without oversight" versus expert selection. The novel-candidate section says the system worked "without oversight" p. 15, but experts then selected the three candidates tested p. 15. Oncologists also reviewed 30 hypotheses p. 13. Count the human selection when comparing with fully automated work.
• Figure and table disagree on KG-1a synergy. Fig. 4b shows only points with CI below 1 for JNJ-64619178 + Selinexor in KG-1a. ext. tab. 2 calls the same combination "mixed response". The paper does not say what dose or effect range would show antagonism fig. 4b [ext. tab. 2].
• ± values are undefined. The IC50 values in ext. fig. 3 and ext. fig. 4 carry "±" terms (for example 0.02 µM ± 29.1). The captions do not define them. Use the 95% CIs in fig. 3 instead.
• Small evaluations. Expert ratings cover 11 goals, and the LLM judge and expert comparisons cover 15. The experts who wrote the goals also rated the outputs p. 11. The expert evaluation was blinded p. 39; blinding did not apply to the cell-viability assays p. 39.
• Hidden compute and human cost. The authors say other models needed "significantly less compute and reasoning time" p. 8, but give no compute figures for Co-Scientist. The only human-time figures are under one hour for the prompt and about three hours for the review p. 36–37.
• Model dependence. All reported agent runs use Gemini 2.0 variants p. 29. Statements about newer Gemini models and other frontier models are expectations, not results p. 24, p. 31. Different experiments name different Gemini 2.0 snapshots (for example "Flash Thinking Experimental 12-19" and "01-21") p. 8, p. 11.
• Safety and illustrative examples. The ALS example was reviewed by domain experts but is still illustrative and may contain errors. It is not meant to suggest therapies, and should be read with utmost caution p. 30. Safety details are in the supplement p. 25.
• Data. The three real-world validation datasets are not public. Public data used include GPQA diamond, DepMap Q2 2024 and Open Targets p. 40.

Lineage

• Builds on: the Elo rating system (ref. 30) p. 34; multi-agent debate for more truthful answers (ref. 35) p. 34; test-time compute scaling (refs. 16 to 18) p. 3. None of these has a card in this wiki.
• Compared against: Gemini 2.0 Pro Experimental, Gemini 2.0 Flash Thinking Experimental 12-19, OpenAI o1, o3-mini-high, DeepSeek R1, and expert "best guess" hypotheses p. 8, p. 11.
• Superseded by: not known from the content of this wiki.