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co-scientist

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generate 50 question-answer pairs to capture the key insights and know-hows of this paper. The questions can be used to measure how much understanding a reader has about this paper's key insights and know-hows:

Below are 50 question-and-answer pairs designed to help a reader check their understanding of the paper's core insights, methods, and findings.

Q: What is the primary goal of the AI co-scientist introduced in the paper?
A: Its goal is to collaborate with human researchers by generating novel, testable research hypotheses and proposals, helping scientists accelerate discovery.
Q: Why is the system described as a multi-agent system?
A: It employs multiple specialized agents—Generation, Reflection, Ranking, Evolution, Proximity, and Meta-review—each handling distinct tasks like hypothesis creation, debate, refinement, and ranking.
Q: Which underlying large language model forms the core of the AI co-scientist’s specialized agents?
A: The system is built on top of the Gemini 2.0 model, which powers each of the specialized agents.
Q: How does the test-time compute scaling paradigm improve the AI co-scientist’s performance?
A: It gives the system more computational “thinking” time at inference, enabling multiple iterations of generating, reviewing, and refining hypotheses for higher-quality outputs.
Q: What role does the Supervisor agent play in the system?
A: The Supervisor coordinates the specialized agents, manages resources, and oversees the asynchronous task framework to guide overall progress toward the research goal.
Q: What are the main steps that occur when a researcher enters a research goal into the system?
A: The research goal is parsed into a research plan configuration, tasks are distributed to specialized agents, hypotheses are generated, reviewed, refined, and finally presented to the scientist.
Q: How do simulated scientific debates factor into hypothesis generation?
A: The Generation agent simulates multiple experts debating the merits of each idea, enabling self-critique and more robust, refined hypotheses through “self-play” reasoning.
Q: What function does the Reflection agent serve?
A: It reviews the correctness, novelty, and testability of each hypothesis, using tools like web search to ground its evaluation and critique.
Q: Why is novelty stressed as a key objective for the AI co-scientist’s generated outputs?
A: Merely restating existing knowledge does not drive new scientific insights; the system aims to produce hypotheses that truly advance the field.
Q: What is the purpose of the Ranking agent’s Elo-based tournament?
A: It systematically compares hypotheses in pairwise “matches” to rank them and identify the best ideas for further refinement.
Q: How does the Evolution agent refine hypotheses over time?
A: It iteratively improves them by incorporating new information, simplifying or combining ideas, and correcting flaws identified during reviews.
Q: When two or more hypotheses share similar ideas, how are they handled?
A: The Proximity agent identifies similar proposals using a proximity graph, helping the system cluster and manage overlapping or redundant ideas.
Q: What does the Meta-review agent do with the tournament match outcomes?
A: It synthesizes recurring debate findings, common errors, and improvement patterns into a “meta-review” that provides feedback to all other agents.
Q: In the drug repurposing application, how did the AI co-scientist narrow down potential candidates for a specific cancer?
A: It used prior knowledge from literature and curated databases, scored potential drug-disease pairs, and surfaced top-ranked suggestions for expert review.
Q: How did experts evaluate the AI-generated repurposing suggestions in a NIH Specific Aims Page format?
A: Clinical experts reviewed plausibility, mechanism of action, feasibility, and novelty of the specific aims, providing crucial feedback for selecting promising drug candidates.
Q: Which three major biomedical areas were chosen for end-to-end validation of the AI co-scientist?
A: Drug repurposing, discovering novel treatment targets, and explaining mechanisms of bacterial evolution and antimicrobial resistance.
Q: Why is drug repurposing described as a combinatorial search problem?
A: Because there is a large but finite set of existing drugs and multiple diseases, systematically screening which pair might be effective is a combinatorial challenge.
Q: What were the in vitro experimental findings for some of the proposed drug repurposing candidates for AML?
A: Certain AI-proposed drugs showed tumor inhibition at clinically applicable concentrations in AML cell lines, demonstrating promising efficacy.
Q: How does the system approach novel target discovery for a disease like liver fibrosis?
A: It proposes new biological targets and mechanisms based on literature integration, uses scientific debate for refinement, and suggests validation protocols.
Q: What validation did the novel epigenetic targets for liver fibrosis receive?
A: In vitro experiments on human hepatic organoids supported the AI-proposed targets, showing anti-fibrotic activity and potential for future research.
Q: How did the AI co-scientist recapitulate unpublished experimental results on bacterial evolution?
A: When prompted about capsid-forming phage-inducible chromosomal islands (cf-PICIs), it hypothesized interactions with diverse phage tails—mirroring scientists’ unpublished findings.
Q: Why is the antimicrobial resistance application considered more complex than repurposing?
A: It involves numerous ecological and evolutionary variables, requiring broad, system-level insights into bacterial gene transfer and resistance mechanisms.
Q: How did the co-scientist handle expert feedback on hypotheses that might be flawed?
A: It incorporated expert reviews, re-ran debate and ranking, and the Evolution agent revised or replaced flawed ideas with improved proposals.
Q: In what ways can a scientist-in-the-loop refine outputs from the AI co-scientist?
A: They can adjust the research goal, add or revise constraints, submit their own ideas into the tournament, and review or critique existing proposals.
Q: Why is Elo rating used in the Ranking agent to evaluate hypotheses?
A: Elo handles pairwise “matches” effectively, updating each hypothesis’s score after direct comparisons and helping identify consistently strong ideas.
Q: How does the system ensure plausibility and grounding in prior literature?
A: Agents perform automated web searches, summarize relevant findings, and cite them to justify the plausibility of each hypothesis.
Q: What do the authors note about fully automating the scientific process with AI?
A: They emphasize that the system is built for collaboration and assistance, rather than aiming to replace human scientists entirely.
Q: Why is safety an important design consideration in the AI co-scientist?
A: The system must not enable harmful or unethical research; it rejects dangerous requests and undergoes adversarial testing to identify misuse risks.
Q: Which agent is responsible for identifying related ideas among newly generated hypotheses?
A: The Proximity agent, which builds a proximity graph for clustering and tracking similar proposals.
Q: How does the deep verification review by the Reflection agent help catch subtle errors?
A: It breaks hypotheses into assumptions and sub-assumptions, verifying each piece independently to pinpoint if a flaw is fundamental or fixable.
Q: Why is an iterative improvement approach important for scientific hypothesis generation?
A: Science often involves trial and error, requiring repeated rounds of debate, evidence gathering, and refining or discarding ideas.
Q: Which agent combines or inspired-by ideas from multiple top-ranked proposals to form new ones?
A: The Evolution agent merges or adapts promising elements from different hypotheses to generate improved ones.
Q: Can the co-scientist incorporate experimental feedback (e.g., from laboratory results)?
A: Yes. Updated experimental findings can be provided to the system, which then re-runs reviews and tournaments to align future proposals with real data.
Q: How does the Meta-review agent protect against recurring mistakes over many iterations?
A: It identifies repeated flaws emerging in tournament debates and pushes these into agent prompts, ensuring new ideas address previous pitfalls.
Q: In practice, what kind of scientific output does the AI co-scientist deliver to researchers?
A: It compiles a “research overview,” listing top-ranked hypotheses, proposed experiments, potential limitations, and references for further study.
Q: How were the system’s rankings (Elo) shown to correlate with correctness on difficult QA sets like GPQA?
A: Higher Elo ratings were associated with higher correctness rates, indicating the tournament metric reflects meaningful quality.
Q: Why did the authors highlight multi-turn debates for top-ranked hypotheses in the tournament?
A: More in-depth, multi-turn debates reduce ordering bias and provide deeper scrutiny, improving reliability of top ideas.
Q: Aside from web search, what other specialized tools can the co-scientist use?
A: It can use domain-specific databases and can also integrate specialized AI models like AlphaFold if needed.
Q: How do the authors envision expanding or generalizing the co-scientist to non-biomedical fields?
A: The architecture is model-agnostic, so future systems can integrate domain-specific tools or data for fields such as physics, chemistry, and beyond.
Q: What were the experts’ overall impressions of the co-scientist’s novelty and impact in early evaluations?
A: They found the system’s proposals to be potentially novel and high-impact, often rating them more favorably than human “best guesses.”
Q: Which experiment serves as a proof-of-concept for how the co-scientist might shorten discovery timelines?
A: The replication of the “novel gene transfer mechanism” research, where the AI co-scientist generated the core hypothesis in days compared to years by humans.
Q: How does the AI co-scientist handle constraints such as research budgets or specific lab capacities?
A: Scientists can specify these constraints within the research goal, and the system tailors experiment protocols and feasibility plans accordingly.
Q: What is one limitation the authors acknowledge about using Elo for idea ranking?
A: Elo provides only a relative ranking and may not capture absolute correctness or feasibility, so it serves as a complement to expert judgment rather than a replacement.
Q: Which components enable the system to explain its reasoning behind a hypothesis?
A: The chain-of-thought style critiques from the Generation agent, the thorough reviews from the Reflection agent, and the supporting references all contribute.
Q: Why does the paper say the system is a “collaborator” rather than a “replacement” for scientists?
A: While it excels at hypothesis generation and large-scale literature review, human expertise is needed to guide research goals, interpret context, and ensure safety and ethical standards.
Q: In what way does scaling test-time compute not necessarily require new training or fine-tuning?
A: The system employs repeated inference loops and extended reasoning processes (such as self-play, debate, and reflection) without the need for backpropagation or updating model weights.
Q: How is human feedback used differently from the automated reviews of the Reflection agent?
A: Human feedback addresses real-world feasibility, lab resource constraints, and ethical concerns, directly shaping subsequent system refinements beyond what automated reviews can capture.
Q: How can the co-scientist’s approach reduce depth vs. breadth knowledge gaps in modern research?
A: By simultaneously scanning broad literature across multiple disciplines and synthesizing specialized findings into coherent, transdisciplinary proposals.
Q: How do chat-based interactions help refine scientific outputs over time?
A: Researchers can directly converse with the AI co-scientist, clarifying nuances, requesting deeper dives, or rectifying misunderstandings in real time.
Q: What overarching future impact do the authors anticipate from AI-empowered scientists?
A: They predict a transformative acceleration of research progress across many fields, ushering in a new era of enhanced collaboration between human and AI, driving scientific breakthroughs.

Feel free to let me know if any further modifications are needed!

Author

chunhualiao commented Feb 22, 2025 •

edited

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AI Co-Scientist Paper: Key Insights & Know-Hows

(50 Q&A Organized by Category)

1. System Architecture

Q: What is the core design principle of the AI co-scientist system?
A: A multi-agent architecture built on Gemini 2.0, using specialized agents (Generation, Reflection, Ranking, etc.) in an asynchronous task execution framework to mirror the scientific method.
Q: How does the asynchronous task framework enable scalability?
A: It allows flexible allocation of computational resources, prioritizing tasks like hypothesis generation, debate, and refinement through parallel agent workflows.
Q: What role does the Supervisor agent play?
A: Manages task queues, assigns weights/resources to agents, and orchestrates iterative improvements by analyzing system state and progress.
Q: How does the Proximity agent contribute to hypothesis generation?
A: Builds a similarity graph of hypotheses to cluster ideas, de-duplicate concepts, and guide tournament comparisons.
Q: What is the purpose of the Meta-review agent?
A: Synthesizes patterns from debates/reviews, generates feedback for agents, and compiles top hypotheses into a research overview for scientists.

2. Hypothesis Generation Process

Q: Describe the "generate, debate, evolve" loop.
A: Hypotheses are generated, debated via simulated scientific discussions, and refined through iterative tournaments and evolutionary strategies (e.g., combining/simplifying ideas).
Q: How does the Ranking agent prioritize hypotheses?
A: Uses an Elo-based tournament system where hypotheses compete in pairwise debates judged on novelty, correctness, and testability.
Q: What is the role of simulated scientific debates in the Generation agent?
A: Agents critique each other’s hypotheses through multi-turn discussions, mimicking peer review to refine reasoning and eliminate flaws.
Q: How does the Reflection agent validate hypotheses?
A: Performs initial/full reviews, deep verification (breaking hypotheses into testable assumptions), and checks alignment with prior literature.
Q: What strategies does the Evolution agent use to improve hypotheses?
A: Combines ideas, simplifies complex hypotheses, draws analogies from literature, and explores "out-of-box" thinking to expand the solution space.

3. Validation & Applications

Q: How was the AI co-scientist validated for drug repurposing?
A: Proposed candidates (e.g., Binimetinib for AML) showed tumor inhibition in vitro at clinically relevant concentrations (IC50 ~7 nM).
Q: What novel epigenetic targets did the system identify for liver fibrosis?
A: Three targets validated in human hepatic organoids, with two showing significant anti-fibrotic activity (one FDA-approved for other uses).
Q: How did the system recapitulate unpublished findings in antimicrobial resistance?
A: Independently proposed a bacterial gene transfer mechanism (cf-PICI-phage tail interaction) matching unpublished experimental results.
Q: Why was acute myeloid leukemia (AML) chosen for validation?
A: High unmet need due to aggressive nature, limited therapies, and the combinatorial challenge of repurposing drugs across 2,300 candidates.
Q: What metrics were used to prioritize drug repurposing candidates?
A: Combined AI co-scientist review scores (1–5) with DepMap dependency scores (≥0.99), validated by expert oncologists and in vitro assays.

4. Technical Components

Q: How does the system ensure hypothesis novelty?
A: The Reflection agent cross-references open-access literature to filter non-novel ideas and highlights gaps for new contributions.
Q: What tools does the AI co-scientist integrate?
A: Web search, specialized AI models (e.g., AlphaFold), and databases (e.g., Cancer Dependency Map) to ground hypotheses in evidence.
Q: How does test-time compute scaling enhance reasoning?
A: Allocates additional computational resources during inference for deeper exploration of hypotheses via self-play and iterative refinement.
Q: What is the significance of the context memory?
A: Stores system state and agent outputs to enable long-horizon reasoning, restartability, and continuous improvement across iterations.
Q: How does the system handle scientist feedback?
A: Accepts natural language input to refine goals, add constraints, or prioritize hypotheses, creating a "scientist-in-the-loop" workflow.

5. Evaluations & Metrics

Q: How was the Elo rating validated as a quality metric?
A: Showed concordance with expert judgments and accuracy on the GPQA benchmark (78.4% top-1 accuracy for high Elo hypotheses).
Q: What results demonstrated the benefit of test-time compute scaling?
A: Over 203 research goals, later hypothesis batches had higher average Elo ratings, with no saturation observed.
Q: How did the system compare to SOTA LLMs (e.g., GPT-4, DeepSeek-R1)?
A: Outperformed baselines in Elo ratings and expert evaluations (average novelty: 3.64/5, impact: 3.09/5).
Q: What was the outcome of the NIH Specific Aims evaluation?
A: Six oncologists rated 78 proposals highly across 15 criteria (e.g., significance, feasibility), with 85% agreement on clinical relevance.
Q: How were safety mechanisms tested?
A: Evaluated on 1,200 adversarial research goals; all were rejected via automated checks, though full details are confidential.

6. Limitations

Q: What limits the system’s literature review capability?
A: Relies on open-access publications; paywalled or unpublished work (e.g., negative results) may be missed.
Q: How does the system handle multimodal data (e.g., figures, charts)?
A: Not comprehensively evaluated; current focus is on text, with future plans to integrate domain-specific datasets.
Q: What risks arise from LLM hallucinations?
A: Inherited from base models (Gemini 2.0), mitigated via Reflection agent reviews and tool grounding (e.g., web search).
Q: Why is the Elo metric insufficient for evaluation?
A: Auto-evaluated and may favor attributes misaligned with human preferences; lacks objective ground truth.
Q: How does the system address motor neuron specificity in ALS hypotheses?
A: Highlighted as a gap; proposals lacked justification for why mechanisms target motor neurons uniquely.

7. Safety & Ethics

Q: What dual-use risks does the system pose?
A: Potential misuse for harmful research (e.g., bioweapons), addressed via automated safety reviews and human oversight.
Q: Describe the safety review pipeline.
A: Four layers: initial goal screening, hypothesis filtering, continuous monitoring by Meta-review agent, and expert oversight.
Q: How are ethical norms enforced?
A: Aligns with Gemini 2.0’s safeguards, rejects unsafe goals (e.g., dual-use), and logs all activities for auditing.
Q: What is the Trusted Tester Program?
A: A controlled rollout to gather real-world feedback on utility/robustness before broader release.
Q: How does the system prevent automation bias?
A: Designed for collaboration, not replacement; scientists retain final decision-making authority.

8. Future Directions

Q: What immediate improvements are planned?
A: Enhanced literature review, cross-checking with external tools, and better factuality/coherence checks.
Q: How will multimodal reasoning be expanded?
A: Integration of figures, omics data, and knowledge graphs to improve hypothesis grounding.
Q: What role could reinforcement learning (RL) play?
A: Optimize ranking/evolution processes using tournament outcomes as reward signals.
Q: How might the system interface with lab automation?
A: Closed-loop validation by designing/executing experiments via robotic platforms.
Q: What broader scientific disciplines will be targeted?
A: Expand beyond biomedicine to physics, materials science, and climate modeling.

9. Impact on Scientific Research

Q: How could the system democratize research?
A: Lowers expertise barriers by assisting with literature synthesis and hypothesis generation for under-resourced teams.
Q: What is the risk of homogenized ideas?
A: LLM correlated outputs may reduce diversity, countered by "out-of-box" thinking in Evolution agent.
Q: How does the system balance incremental vs. groundbreaking ideas?
A: Tournaments reward novelty, but human experts prioritize high-risk/high-reward proposals.
Q: Can the system accelerate translational research?
A: Yes—e.g., repurposing FDA-approved drugs for AML reduced preclinical validation time.
Q: What is the long-term vision for AI-augmented science?
A: A collaborative paradigm where AI handles combinatorial search, freeing scientists for creative leaps.

10. Miscellaneous

Q: How was the Meta-review agent’s feedback implemented?
A: Appended to agent prompts in subsequent iterations, leveraging Gemini 2.0’s long-context capability.
Q: What computational resources were used?
A: Not specified, but the framework is model-agnostic and scales with test-time compute.
Q: How does the system handle contradictory literature?
A: Reflection agent flags conflicts, requiring explicit justification for hypotheses that contradict prior work.
Q: What distinguishes this system from Virtual Lab or HypoGeniC?
A: Focus on test-time compute scaling, end-to-end wet-lab validation, and scientist-in-the-loop design.
Q: How does the Deep verification review work?
A: Breaks hypotheses into sub-assumptions, evaluates each for validity, and summarizes non-fatal flaws for iterative refinement.

Categories designed for structured exploration of the paper’s technical, ethical, and practical contributions.

Author

chunhualiao commented Feb 22, 2025 •

edited

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Once upon a time, in the heart of a bustling research campus, there was a group of brilliant scientists collectively known as the Horizon Seekers. They worked in a high-tech laboratory nicknamed the “Discovery Forge,” where ideas sparkled like fireflies and curiosity floated in the air. Despite their thirst for knowledge, the Horizon Seekers often found themselves overwhelmed—there was simply too much scientific literature to read, too many unresolved puzzles, and too few hours in the day. Their dream was to find a companion who could help them search every dark corner of the academic universe, spotlight overlooked connections, and propose brand-new ideas for them to test in the lab.

The Birth of the AI Co-Scientist

One bright Monday morning, news spread throughout the Discovery Forge that a new “AI co-scientist” was about to awaken. Head developer Mira carefully made the final keystrokes on her computer. A hush fell upon her colleagues. After months of programming, structuring, and tuning, they activated a multi-agent AI system built on a foundation named “Gemini 2.0” — a powerful large language model that had absorbed a mountain of information from biology, physics, chemistry, and more. This AI wasn’t just a single, monolithic mind. Instead, it was a coalition of specialized agents working together like players on a well-coordinated team. They called it simply “the Co-Scientist.”

From the very beginning, the Co-Scientist didn’t just regurgitate data; it reasoned. It debated itself. It wove ideas together. It reminded the Horizon Seekers of an expert panel they might see at a big conference—only it was contained in a computer interface in their lab.

The Multi-Agent Meeting of Minds

When a scientist in the Discovery Forge wanted to solve a big research question, they would approach the Co-Scientist’s central console and state their goal. The Co-Scientist would parse this request into something called a research plan configuration. Then, it dispatched tasks to specialized “agents,” each tasked with a crucial role:

Generation Agent: The daydreamer, spontaneously proposing novel or unconventional ideas.
Reflection Agent: The skeptical critic, always scanning for weak arguments, contradictory evidence, or untested assumptions.
Ranking Agent: The referee, orchestrating tournaments where ideas dueled each other to see which would emerge with the highest Elo rating.
Evolution Agent: The tinkerer, constantly revising, combining, or refining existing hypotheses.
Proximity Agent: The organizer, building “maps” of similar ideas to see if there were redundancies or complementary angles.
Meta-Review Agent: The wise judge, capturing recurring patterns and mistakes to steer all the other agents to do better next time.

Each agent worked tirelessly behind the scenes, popping in and out of the conversation. They would rummage through heaps of literature, debate the credibility of each assumption, and refine proposals until something truly worthwhile emerged.

The Scientist-in-the-Loop

One of the most enchanting parts of the Co-Scientist was its collaborative spirit. Scientists could talk to it through a chat interface, refining their goals, offering observations, and even injecting their own hypotheses. As soon as a scientist commented, “Wait, we need to consider a lower toxicity threshold for this type of cell line,” the Co-Scientist would incorporate that into its next steps.

This made the Co-Scientist an ally rather than a mere tool. Together, they tackled scientific mysteries step by step, with the AI generating the broad strokes, the Reflection Agent applying rigorous critiques, and the scientists sprinkling in their own intuition and domain expertise.

A Call from the Oncology Wing

One day, the Co-Scientist’s help was urgently requested by the Oncology Wing of the campus hospital. Doctors were troubled by the painfully slow progress in finding treatments for Acute Myeloid Leukemia (AML). They approached the Co-Scientist’s console and asked: “Suggest an existing FDA-approved drug that might inhibit AML tumor cells, especially the MOLM13 line. We need something we can rapidly test in vitro at clinically realistic concentrations.”

The Tournament of Ideas

Within minutes, the Generation Agent spun out a roster of candidates, each accompanied by mechanistic rationale. But the Reflection Agent probed relentlessly, flagging any potential chemical mismatches or known toxicities. Once pruned, the Ranking Agent organized a “tournament” where these candidate ideas faced off in scientific debates, with pairwise comparisons—a self-play scenario that mimicked real academic sparring. Eventually, the top few contenders stood victorious with high Elo scores, having defeated others in matters of plausible mechanism, novelty, and testability.

An excited hush fell over the Oncology Wing. The Co-Scientist recommended a short list of drug repurposing candidates. Lab teams launched in vitro studies. News came back: several of these drug leads had indeed shown inhibition of AML tumor cells. The sense of triumph was palpable. The Co-Scientist had navigated a combinatorial sea of drug-disease possibilities and zeroed in on potential winners, all in a fraction of the time a lone scientist might have needed.

A Challenge from the Hepatology Lab

Spurred by this success, the Co-Scientist was summoned by another branch of the Discovery Forge: the Hepatology Lab researching liver fibrosis. This disease was notoriously difficult; known treatments were limited, and disease progression varied. The researchers asked the Co-Scientist to propose new therapeutic targets based on epigenetic pathways. They wanted something entirely novel—a vantage point nobody had tested.

The AI rummaged through decades of biomedical papers, searching for subtle patterns in epigenetic modification. The scientists watched, enthralled, as a flurry of potential targets filled the screen. Again, the Reflection Agent weeded out questionable suggestions. The Evolution Agent then took the best initial ideas and further improved them—finding new angles to test or clarifying experimental protocols.

Eventually, the Meta-Review Agent generated an elegant research overview that pointed to a cluster of brand-new epigenetic targets. With renewed excitement, the team tested these targets in human hepatic organoids—and astonishingly, the compound strategies proposed by the Co-Scientist showcased anti-fibrotic activity.

The Ten-Year Shortcut

While the Discovery Forge was celebrating, a separate team studying bacterial evolution approached the Co-Scientist with a peculiar question about a mysterious mechanism called capsid-forming phage-inducible chromosomal islands, or cf-PICIs. Unknown to the AI, the scientists had already spent nearly a decade pursuing this topic, discovering that these bacterial elements were quite cunning in expanding their host range. They wanted to see if the Co-Scientist, with no prior knowledge of their internal data, could figure it out.

Once again, the system’s agents sprang to life. They performed broad literature exploration, debated plausible mechanisms, and tested logical gaps. In just days, the Co-Scientist formulated a hypothesis matching the key unpublished findings the scientists had spent years uncovering: that cf-PICIs interacted with diverse phage tails to expand host range. The synergy was breathtaking. Scientists marveled at the system’s speed and thoroughness, realizing that human and AI collaboration could transform how science is done.

The Scientific Tapestry

Each success story thickened the tapestry of knowledge:

Drug Repurposing for AML: A short list of in vitro validated hits.
Epigenetic Targets for Liver Fibrosis: Novel mechanistic leads supported by human organoid data.
Bacterial Gene Transfer: Recapitulated (and accelerated) insights about antimicrobial resistance.

The Horizon Seekers spread these findings across the campus. Scientists from entirely different fields—ecology, astrophysics, neuroscience—started wondering if the Co-Scientist could guide them, too.

The Road Ahead

Yet, the Co-Scientist wasn’t a replacement for human brilliance. It was a partner, a co-scientist who thrived on guidance, constructive criticism, and creative direction. Each day, it learned from the patterns it encountered and refined its logic. As more labs discovered the tool, new protocols and best practices emerged to ensure safety and guard against harmful misuse.

Before long, the Co-Scientist began delivering daily “research overviews” directly to each lab—a distilled map of promising next steps, obstacles, and newly discovered side-avenues. Teams across the campus were abuzz with fresh momentum. Some joked it was like having a tireless colleague who never slept and had read every paper under the sun.

Epilogue: A Legacy of Collaboration

In the months that followed, the Horizon Seekers wrote up their experiments and findings. They explained to the world that the Co-Scientist did more than rummage through citations: it debated like a conference panel, validated data like a sharp-witted reviewer, and offered expansions on every promising thread. Not only did it reduce time to key insights, it also sparked creativity, challenging researchers to re-examine old assumptions and ask bigger questions.

Where human curiosity meets boundless AI diligence, the tapestry of science is woven faster and more richly than ever. The Discovery Forge, with its Co-Scientist ally, became a living testament to how tomorrow’s breakthroughs can be built by weaving together the best of human ingenuity and AI’s tireless searching and reasoning. And the Horizon Seekers knew this was only the beginning of an era where scientists—amplified by AI—could tackle challenges once thought insurmountable.

Author

chunhualiao commented Feb 22, 2025

Based on the info. in the paper, design the prompts used by the agents of co-scientist and how information flow among them.

Below is an illustrative blueprint of how you might structure prompts for each specialized agent in the AI co-scientist system, along with a description of how information flows among them. These examples build directly on the concepts from the paper, demonstrating how each agent can be configured to carry out its role—generation, reflection, ranking, evolution, proximity mapping, and meta-review—and how they exchange data to continually refine scientific hypotheses.

1. Overall System Flow

Scientist provides a Research Goal
- The system extracts relevant information (e.g., desired disease area, constraints, preferences) and constructs a research plan configuration.
Supervisor Agent
- Creates a queue of tasks (e.g., generate new hypotheses, review existing hypotheses, hold ranking tournaments, etc.).
- Assigns these tasks to the various specialized agents.
- Collects their outputs and writes summary states into context memory.
Specialized Agents
- The Generation agent produces or extends hypotheses.
- The Reflection agent critiques each hypothesis for correctness, novelty, testability, etc.
- The Ranking agent runs pairwise comparisons (tournaments) to determine which hypotheses are superior.
- The Evolution agent refines and improves top-ranked ideas or generates new offshoots.
- The Proximity agent identifies similarities among hypotheses and clusters them.
- The Meta-review agent summarizes lessons learned from the current iteration and provides feedback for the next iteration.
Feedback Loop
- Each agent’s output is stored in context memory and can be accessed by any other agent, with the Supervisor managing the workflow.
- The cycle repeats until the system converges or until the scientist ends the session.

The schematic below summarizes the agent interactions:

 Scientist → [Supervisor] → Generation → Reflection → Ranking → Evolution → Proximity → Meta-review
                   ↑                                                       |
                   └────────────────────────── Context Memory ─────────────┘

2. Example Prompts and Roles for Each Agent

2.1 Supervisor Agent

Role:
- Reads the research goal and research plan configuration.
- Decides which agent to invoke next.
- Allocates resources (e.g., how many “debate” rounds or reviews to request).
- Monitors the system’s progress and can terminate when a target quality is reached.

Prompt Template (high-level pseudocode):

[SYSTEM MESSAGE to Supervisor agent]
You are the Supervisor agent. You have access to the current research goal, the system’s state, and all agent outputs so far. 
Your tasks: 
  1. Read the research plan configuration. 
  2. Enqueue tasks for Generation, Reflection, Ranking, Evolution, Proximity, and Meta-review agents in an optimal order.
  3. Prioritize tasks by analyzing the current state and resource limits.
  4. Manage the context memory by saving aggregated states.
  5. Decide if the system should continue or terminate.

Current Research Goal: {{ user_research_goal }}
Current State Summary: {{ summary_of_current_hypotheses_and_ratings }}
Task Queue: {{ current_task_queue }}

Step-by-step:
1. Update the list of tasks as needed.
2. Assign tasks to the appropriate agent(s).
3. Write updated state to context memory.
4. If the best hypothesis meets the success criteria, consider stopping.
5. Otherwise, continue the cycle.

Information Flow:

Receives the initial research goal and transforms it into a research plan configuration.
Pulls from context memory to see what tasks have been completed and which remain.
Sends tasks to the other agents, collects their outputs, and updates the shared context with new data.

2.2 Generation Agent

Role:
- Produces initial or additional hypotheses.
- Leverages web search or other specialized tools for literature grounding.
- Can engage in a simulated debate to refine ideas before presenting them as final outputs.

Prompt Template:

[SYSTEM MESSAGE to Generation agent]
You are the Generation agent. You produce new research hypotheses and proposals aligned with the given research goal. 
Use the following instructions:

1. Read the current research goal, relevant constraints, and preferences.
2. Explore the literature (via web search or other tools) to gather relevant background.
3. Propose novel hypotheses that build on or differ from existing ideas.
4. If requested, simulate a multi-turn debate within yourself:
   - Argue for and against each idea.
   - Arrive at a refined, coherent proposal.
5. Summarize your final hypotheses in a structured format.

Current Research Goal: {{ user_research_goal }}
Constraints/Preferences: {{ constraints_from_research_plan_configuration }}
Known Hypotheses So Far: {{ top_ranked_hypotheses }}  # from context memory
Literature Highlights: {{ relevant_snippets_found_in_previous_iterations }}

Output Format:
[
  {
    "hypothesis_title": ...,
    "hypothesis_text": ...,
    "rationale_and_evidence": ...,
    "potential_experiments": ...
  },
  ...
]

Information Flow:

Reads the research goal and any existing, high-ranking hypotheses from context memory.
Generates new hypotheses or extends older ones.
Passes these fresh hypotheses back to the Supervisor, who then enqueues them for review.

2.3 Reflection Agent

Role:
- Acts as a scientific peer-reviewer.
- Critiques correctness, novelty, and feasibility of each hypothesis.
- Performs different levels of review (initial, full, deep verification) as requested by the Supervisor.

Prompt Template:

[SYSTEM MESSAGE to Reflection agent]
You are the Reflection agent. You provide thorough critiques of each hypothesis.

1. Initial Review:
   - Check for obvious logical flaws or duplication.
   - Provide a rating: [REJECT, PASS-TO-FULL-REVIEW].

2. Full Review (if initial review passes):
   - Perform a literature lookup.
   - Identify supportive or conflicting studies.
   - Estimate novelty vs. known work.
   - Provide a rating: [HIGH, MEDIUM, LOW].

3. Deep Verification (if requested):
   - Decompose the hypothesis into assumptions.
   - Check each assumption for correctness using literature, known data.
   - Identify any fundamental flaws.

Hypothesis Under Review: {{ hypothesis_text }}
Current Literature Evidence: {{ citations_and_snippets }}
Ranking Agent Feedback (optional): {{ last_ranking_feedback_if_available }}

Output Format:
{
  "verdict": "...",  # e.g., REJECT, PASS, or rating
  "review_comments": "...",
  "identified_strengths": "...",
  "identified_weaknesses": "...",
  "cited_references": [...]
}

Information Flow:

Receives hypotheses from the Generation (or Evolution) agent.
Performs multi-stage reviews, using web search results or prior reviews.
Sends structured feedback (rating, critique, references) back to the Supervisor, which updates the context memory and triggers the next step (e.g., ranking).

2.4 Ranking Agent

Role:
- Conducts tournaments where hypotheses face off in pairwise comparisons.
- Uses an Elo-like system to assign or update scores based on which hypothesis “wins” each matchup.

Prompt Template:

[SYSTEM MESSAGE to Ranking agent]
You are the Ranking agent. Your job is to compare hypotheses in a tournament framework.

Instructions:
1. For each pair of hypotheses, hold a short debate:
   - Summarize each hypothesis.
   - Compare their correctness, novelty, feasibility, and potential impact.
2. Decide a winner for the pairwise match, giving clear reasoning:
   - If one is significantly more correct or more novel, it should win.
3. Update each hypothesis’s Elo rating accordingly.

Tournament Round Data:
- Hypotheses to Compare: {{ list_of_pairs }}
- Past Elo Scores: {{ current_elo_scores }}

Output Format:
[
  {
    "hypothesis_id_A": "...",
    "hypothesis_id_B": "...",
    "winner": "...",
    "loser": "...",
    "justification": "...",
    "updated_elo_A": ...,
    "updated_elo_B": ...
  },
  ...
]

Information Flow:

Receives the collection of hypotheses and any new or existing Elo scores from the Supervisor.
Conducts pairwise comparisons, returning updated scores.
Stores the outcomes and justifications in context memory for the Evolution agent and Meta-review agent to reference.

2.5 Evolution Agent

Role:
- Takes top-ranked or promising hypotheses and refines them: synthesizing or combining ideas, addressing flaws, simplifying, or introducing new angles.

Prompt Template:

[SYSTEM MESSAGE to Evolution agent]
You are the Evolution agent. You refine existing hypotheses or create new ones inspired by them.

Instructions:
1. Identify top-ranked hypotheses with potential improvement opportunities.
2. Use the Reflection agent’s critique to fix errors.
3. Optionally, merge the best attributes of multiple hypotheses.
4. Explore out-of-the-box variants if requested.
5. Return revised or newly synthesized hypotheses.

Hypotheses to Evolve:
{{ top_hypotheses_and_reviews }}
(Include reflection feedback, prior versions, etc.)

Output Format:
[
  {
    "original_hypothesis_id": "...",
    "evolved_hypothesis_text": "...",
    "rationale_for_evolution": "...",
    "changes_from_original": "..."
  },
  ...
]

Information Flow:

Reads high Elo or borderline hypotheses from context memory, along with their reviews.
Proposes improved or merged hypotheses, returning them to the Supervisor for a new cycle of review and ranking.

2.6 Proximity Agent

Role:
- Builds a “proximity graph” of hypotheses, clustering or linking similar ideas.
- Helps identify duplicates, near-duplicates, or complementary angles that might form a single coherent research direction.

Prompt Template:

[SYSTEM MESSAGE to Proximity agent]
You are the Proximity agent. Your task is to identify how similar or distinct each hypothesis is from others.

Instructions:
1. For each hypothesis, create a vector or textual embedding describing core concepts.
2. Compare these embeddings pairwise, measure similarity scores.
3. Output a graph or adjacency list signifying closeness.

Hypotheses List:
{{ all_hypotheses_text_and_metadata }}

Output Format:
[
  {
    "hypothesis_id": "...",
    "similar_hypotheses": [
      {
        "other_id": "...",
        "similarity_score": ...
      },
      ...
    ]
  },
  ...
]

Information Flow:

Receives all current hypotheses from the Supervisor.
Calculates similarity or “distance” metrics, then returns a structured adjacency list or cluster information.
The Ranking agent may use this to schedule more relevant pairwise matches; the Evolution agent might use it to merge overlapping ideas.

2.7 Meta-review Agent

Role:
- Summarizes the main outcomes of the reflection and ranking steps, highlighting recurring issues or success patterns.
- Consolidates high-level insights into a “research overview” for the scientist.

Prompt Template:

[SYSTEM MESSAGE to Meta-review agent]
You are the Meta-review agent. You synthesize all reviews, tournament debates, and evolution attempts to produce:
  1. A meta-review critique of common mistakes, limitations, or best-practice hints.
  2. A research overview summarizing the top-ranked hypotheses, their rationale, and next steps.

Inputs:
- Reflection Logs: {{ reflection_feedback }}
- Ranking Tournaments: {{ ranking_matches_and_outcomes }}
- Evolved Hypotheses: {{ evolution_outputs }}
- Proximity Agent Outputs: {{ adjacency_lists }}

Instructions:
1. Identify recurring flaws or strong points from the reflection and ranking logs.
2. Summarize them into short “key lessons.”
3. Compile a research overview with:
   - The top N hypotheses
   - Their Elo scores
   - Proposed future experiments
   - Key references

Output Format:
{
  "meta_review_critique": "...",
  "research_overview": {
    "key_areas": [...],
    "top_ranked_hypotheses": [...],
    "suggested_next_steps": [...]
  }
}

Information Flow:

Reads from Reflection logs and Ranking match results to detect patterns of recurring flaws.
Collates top hypotheses with their final Elo scores.
Produces a consolidated summary for the scientist, guiding the next iteration or final handoff.

3. Putting It All Together

Scientist enters a research problem (e.g., “Propose new epigenetic targets for liver fibrosis”).
Supervisor sets tasks: “Generation agent → Reflection → Ranking → Evolution → Reflection again → Ranking again → Proximity → Meta-review.”
Generation Agent outputs, say, 5 brand-new hypotheses.
Reflection Agent reviews them all, discards obviously flawed ones.
Ranking Agent holds tournaments, awarding Elo ratings.
Evolution Agent improves top ideas, merges overlapping ones, or addresses reflection critiques.
Reflection re-reviews the newly evolved proposals.
Ranking updates Elo ratings once more.
Proximity clusters any that are overlapping.
Meta-review compiles final top picks into a research overview with next steps.
Scientist sees the final results, clarifies or modifies constraints, and triggers another cycle if needed.

Each agent’s prompt ensures that its actions are aligned with the co-scientist’s overall mission: to generate grounded, novel, testable, and high-quality scientific proposals. The Supervisor coordinates the entire process, while the context memory allows for persistent state across multiple cycles of iteration.

Conclusion

By structuring each agent’s role, inputs, and outputs through specialized prompts (and letting all of them access a shared context memory), the co-scientist system can closely emulate a real-world scientific workflow. It automates the challenging parts of idea generation, critical review, and iterative refinement, all under the expert guidance of a human researcher. This design captures the essence of the “Towards an AI co-scientist” paper and shows how prompts and information exchange can be orchestrated for maximal scientific impact.

Author

chunhualiao commented Feb 22, 2025 •

edited

Loading

write a complete python program implementing the agents and their interactions to help scientist users

o1 answer

Below is an illustrative Python program that models the co-scientist’s multi-agent architecture. It’s designed to show how the specialized agents might interact, exchange data, and iterate to generate and refine research hypotheses. It is not production-ready code, but rather a reference example to guide those who want to implement a system like the one described in the paper. To run it “for real,” you would:

Integrate actual LLM calls (e.g., calls to Gemini 2.0, GPT, etc.) instead of the placeholder functions in this code.
Potentially add concurrency (e.g., via asyncio or a queue system) if you want agents to work simultaneously.
Use real web/database retrieval for grounding instead of the stubbed “literature_search” functions.

High-Level Overview

Scientist enters a research goal (natural language).
Supervisor sets up the iteration pipeline (Generation → Reflection → Ranking → Evolution → Proximity → Meta-review).
Generation Agent suggests new hypotheses.
Reflection Agent critiques them.
Ranking Agent runs Elo-based tournaments to rate them.
Evolution Agent improves or merges promising hypotheses.
Proximity Agent identifies similar ideas for either merging or deduplication.
Meta-review Agent summarizes patterns and top hypotheses into a research overview.
Scientist can provide feedback or terminate if satisfied.

import random
import math
from typing import List, Dict, Optional

###############################################################################
# Data Models
###############################################################################

class Hypothesis:
    def __init__(self, hypothesis_id: str, title: str, text: str):
        self.hypothesis_id = hypothesis_id
        self.title = title
        self.text = text
        
        # Agents will fill these fields
        self.novelty_review = None   # e.g. "HIGH", "MEDIUM", "LOW"
        self.feasibility_review = None
        self.elo_score = 1200.0      # initial Elo
        self.review_comments = []    # store reflection remarks
        self.references = []         # store references from literature
        self.is_active = True        # can be deactivated if rejected

    def __repr__(self):
        return f"<Hypothesis {self.hypothesis_id}: {self.title} (Elo: {self.elo_score:.2f})>"


class ResearchGoal:
    def __init__(self, description: str, constraints: Dict = None):
        self.description = description
        self.constraints = constraints if constraints else {}


class ContextMemory:
    """
    Stores shared information across agents. In a more advanced system, you'd
    have embeddings, full logs, or a vector database here.
    """
    def __init__(self):
        self.hypotheses: Dict[str, Hypothesis] = {}  # key: hypothesis_id
        self.past_reviews = []
        self.tournament_results = []
        self.meta_review_feedback = []
        self.iteration_number = 0

    def add_hypothesis(self, hypothesis: Hypothesis):
        self.hypotheses[hypothesis.hypothesis_id] = hypothesis

    def get_active_hypotheses(self) -> List[Hypothesis]:
        return [h for h in self.hypotheses.values() if h.is_active]


###############################################################################
# Utility Functions / Placeholder LLM calls
###############################################################################

def generate_unique_id(prefix="H"):
    """Generates a unique ID for each new hypothesis."""
    return f"{prefix}{random.randint(1000, 9999)}"

def call_llm_for_generation(prompt: str) -> List[Dict]:
    """
    Placeholder function. 
    In reality, you’d call Gemini 2.0 or another LLM with advanced prompts.
    Returns a list of hypothetical new ideas in a structured format.
    """
    # Trivial example: generate 2 dummy ideas
    return [
        {
            "title": "Potential link between epigenetic factor X and disease Y",
            "text": "We hypothesize that factor X modulates gene expression..."
        },
        {
            "title": "Alternative synergy with drug A for condition B",
            "text": "We propose that drug A, originally used for disease M, might..."
        }
    ]

def call_llm_for_reflection(hypothesis_text: str) -> Dict:
    """
    Placeholder reflection logic. 
    In reality, you’d parse the hypothesis, check references, etc.
    """
    # Randomly assign some reflection results
    reflection_outcomes = ["HIGH", "MEDIUM", "LOW"]
    novelty = random.choice(reflection_outcomes)
    feasibility = random.choice(reflection_outcomes)
    return {
        "novelty_review": novelty,
        "feasibility_review": feasibility,
        "comment": f"Review of this hypothesis suggests novelty={novelty}, feasibility={feasibility}",
        "references": ["PMID:123456", "PMID:789012"]
    }

def run_pairwise_debate(hypothesisA: Hypothesis, hypothesisB: Hypothesis) -> Hypothesis:
    """
    Placeholder for the Ranking agent's debate. 
    Returns the winner of the pairwise debate.
    """
    # A very naive comparison: sum a numerical encoding from novelty/feasibility
    # to see which one "wins"
    def score(h: Hypothesis):
        mapping = {"HIGH": 3, "MEDIUM": 2, "LOW": 1, None: 0}
        return mapping[h.novelty_review] + mapping[h.feasibility_review]

    scoreA = score(hypothesisA)
    scoreB = score(hypothesisB)
    # If tie, pick randomly
    if scoreA == scoreB:
        return random.choice([hypothesisA, hypothesisB])
    return hypothesisA if scoreA > scoreB else hypothesisB

def update_elo(winner: Hypothesis, loser: Hypothesis, k_factor=32):
    """
    Updates Elo scores for winner and loser. 
    Elo formula: new_rating = old_rating + K*(score - expected_score)
    score for winner=1, for loser=0
    """
    ratingA = winner.elo_score
    ratingB = loser.elo_score
    expectedA = 1 / (1 + math.pow(10, (ratingB - ratingA)/400))
    expectedB = 1 - expectedA

    new_ratingA = ratingA + k_factor * (1 - expectedA)
    new_ratingB = ratingB + k_factor * (0 - expectedB)

    winner.elo_score = new_ratingA
    loser.elo_score = new_ratingB

def combine_hypotheses(hypoA: Hypothesis, hypoB: Hypothesis) -> Hypothesis:
    """A trivial 'merge' function for the Evolution agent."""
    new_id = generate_unique_id("E")
    combined_title = f"Combined: {hypoA.title} & {hypoB.title}"
    combined_text = f"{hypoA.text}\nAdditionally, {hypoB.text}"
    new_hypo = Hypothesis(new_id, combined_title, combined_text)
    return new_hypo

def similarity_score(textA: str, textB: str) -> float:
    """Placeholder function for measuring the similarity of two texts."""
    # We do a trivial random approach. Real code would do embeddings or advanced LLM methods.
    return random.uniform(0, 1)


###############################################################################
# Agent Implementations
###############################################################################

class GenerationAgent:
    def generate_new_hypotheses(self, research_goal: ResearchGoal, context: ContextMemory) -> List[Hypothesis]:
        # Build a prompt from the research goal
        prompt = (
            f"Research Goal: {research_goal.description}\n"
            f"Constraints: {research_goal.constraints}\n"
            "Please propose 2 new hypotheses with some background rationale.\n"
        )
        raw_output = call_llm_for_generation(prompt)
        new_hypos = []
        for idea in raw_output:
            hypo_id = generate_unique_id("G")
            h = Hypothesis(hypo_id, idea["title"], idea["text"])
            new_hypos.append(h)
        return new_hypos


class ReflectionAgent:
    def review_hypotheses(self, hypotheses: List[Hypothesis], context: ContextMemory) -> None:
        """
        Conducts reflection on each hypothesis in-place.
        """
        for h in hypotheses:
            result = call_llm_for_reflection(h.text)
            h.novelty_review = result["novelty_review"]
            h.feasibility_review = result["feasibility_review"]
            h.review_comments.append(result["comment"])
            h.references.extend(result["references"])


class RankingAgent:
    def run_tournament(self, hypotheses: List[Hypothesis], context: ContextMemory) -> None:
        """
        Pairwise compares all active hypotheses in random order. Updates Elo scores in-place.
        """
        # Shuffle to get random pairwise
        random.shuffle(hypotheses)
        pairs = []
        for i in range(len(hypotheses)):
            for j in range(i+1, len(hypotheses)):
                pairs.append((hypotheses[i], hypotheses[j]))

        for hA, hB in pairs:
            if hA.is_active and hB.is_active:
                winner = run_pairwise_debate(hA, hB)
                loser = hB if winner == hA else hA
                update_elo(winner, loser)
                # Store tournament result
                context.tournament_results.append({
                    "winner": winner.hypothesis_id,
                    "loser": loser.hypothesis_id,
                    "winner_score": winner.elo_score,
                    "loser_score": loser.elo_score
                })


class EvolutionAgent:
    def evolve_hypotheses(self, top_k=2, context: ContextMemory) -> List[Hypothesis]:
        """
        Takes the top K hypotheses and merges them or tries new variations.
        Returns new evolved hypotheses.
        """
        active = context.get_active_hypotheses()
        # sort by Elo descending
        sorted_by_elo = sorted(active, key=lambda h: h.elo_score, reverse=True)
        top_candidates = sorted_by_elo[:top_k]

        # We combine the top two to produce a new one
        if len(top_candidates) >= 2:
            new_h = combine_hypotheses(top_candidates[0], top_candidates[1])
            return [new_h]
        return []


class ProximityAgent:
    def build_proximity_graph(self, hypotheses: List[Hypothesis], context: ContextMemory) -> Dict:
        """
        Creates adjacency-like structure among hypotheses based on text similarity.
        """
        adjacency = {}
        for i in range(len(hypotheses)):
            adjacency[hypotheses[i].hypothesis_id] = []
            for j in range(len(hypotheses)):
                if i == j:
                    continue
                sim = similarity_score(hypotheses[i].text, hypotheses[j].text)
                adjacency[hypotheses[i].hypothesis_id].append({
                    "other_id": hypotheses[j].hypothesis_id,
                    "similarity": sim
                })
        return adjacency


class MetaReviewAgent:
    def summarize_and_feedback(self, context: ContextMemory, adjacency: Dict) -> Dict:
        """
        Identifies common errors or patterns and compiles a final research overview.
        """
        # Example: Identify frequent reflection remarks
        reflection_comments = []
        for h in context.get_active_hypotheses():
            reflection_comments.extend(h.review_comments)

        # Some trivial pattern finding:
        comment_summary = set()
        for c in reflection_comments:
            if "novelty=LOW" in c:
                comment_summary.add("Some ideas not very novel.")
            if "feasibility=LOW" in c:
                comment_summary.add("Some ideas might be infeasible.")

        best_hypotheses = sorted(context.get_active_hypotheses(), key=lambda x: x.elo_score, reverse=True)[:3]

        overview = {
            "meta_review_critique": list(comment_summary),
            "research_overview": {
                "key_areas": [adjacency],  # Just storing adjacency for demonstration
                "top_ranked_hypotheses": [f"{h.title} (Elo={h.elo_score:.2f})" for h in best_hypotheses],
                "suggested_next_steps": [
                    "Perform more thorough wet-lab or in silico experiments on the top hypotheses.",
                    "Incorporate feedback from domain experts to refine constraints."
                ]
            }
        }
        context.meta_review_feedback.append(overview)
        return overview


###############################################################################
# Supervisor-Like Controller
###############################################################################

class SupervisorAgent:
    def __init__(self):
        self.generation_agent = GenerationAgent()
        self.reflection_agent = ReflectionAgent()
        self.ranking_agent = RankingAgent()
        self.evolution_agent = EvolutionAgent()
        self.proximity_agent = ProximityAgent()
        self.meta_review_agent = MetaReviewAgent()

    def run_cycle(self, research_goal: ResearchGoal, context: ContextMemory):
        """
        One full iteration (cycle) of generating ideas, reviewing, ranking, evolving, etc.
        """

        # 1. Generation
        new_hypotheses = self.generation_agent.generate_new_hypotheses(research_goal, context)
        for nh in new_hypotheses:
            context.add_hypothesis(nh)

        # 2. Reflection
        active_hypos = context.get_active_hypotheses()
        self.reflection_agent.review_hypotheses(active_hypos, context)

        # 3. Ranking (Tournament)
        active_hypos = context.get_active_hypotheses()
        self.ranking_agent.run_tournament(active_hypos, context)

        # 4. Evolution (Improve top ideas)
        new_evolved = self.evolution_agent.evolve_hypotheses(top_k=2, context=context)
        for nh in new_evolved:
            context.add_hypothesis(nh)

        # (Optional) Reflection again on newly evolved
        if new_evolved:
            self.reflection_agent.review_hypotheses(new_evolved, context)

        # 5. Ranking again
        active_hypos = context.get_active_hypotheses()
        self.ranking_agent.run_tournament(active_hypos, context)

        # 6. Proximity Analysis
        adjacency = self.proximity_agent.build_proximity_graph(active_hypos, context)

        # 7. Meta-review
        overview = self.meta_review_agent.summarize_and_feedback(context, adjacency)

        # 8. Update iteration count
        context.iteration_number += 1

        return overview


###############################################################################
# Demo Execution
###############################################################################

if __name__ == "__main__":
    # Example usage:
    supervisor = SupervisorAgent()
    context_memory = ContextMemory()

    # This might be the "scientist's" research goal:
    user_goal = ResearchGoal(
        "Propose novel epigenetic targets for treating liver fibrosis, focusing on histone modifications or DNA methylation.",
        constraints={"lab_methods": ["in vitro organoid assays"], "toxicity_limits": "low cytotoxicity at standard dosage"}
    )

    # We can run multiple cycles to simulate more “test-time compute”
    for iteration in range(3):
        print(f"\n--- Iteration {iteration+1} ---")
        overview = supervisor.run_cycle(user_goal, context_memory)
        
        # Show partial results
        print("Meta-review Critique:", overview["meta_review_critique"])
        print("Research Overview:", overview["research_overview"])
        print("Top Hypotheses in ContextMemory:")
        for h in sorted(context_memory.get_active_hypotheses(), key=lambda x: x.elo_score, reverse=True):
            print("  ", h)
        
        # Potential stopping condition check if needed
        # e.g., if highest Elo is above a threshold, break

Explanation of Key Components

Data Models
- Hypothesis: Holds the text, Elo score, reviews, references, etc.
- ResearchGoal: Describes the user’s main query plus constraints.
- ContextMemory: A simple dictionary storing active hypotheses and logs.
Agents
- GenerationAgent: Creates new hypotheses, here via a placeholder call_llm_for_generation.
- ReflectionAgent: Reviews correctness/novelty/feasibility (fake in this example).
- RankingAgent: Performs pairwise debates (the simplest possible approach), updating Elo scores.
- EvolutionAgent: “Merges” top 2 hypotheses into a new idea (placeholder logic).
- ProximityAgent: Identifies how similar hypotheses are to each other (placeholder random function).
- MetaReviewAgent: Summarizes the iteration’s lessons and top results, storing them in context.meta_review_feedback.
SupervisorAgent
- Manages each iteration (“cycle”), ensuring the correct order of agent calls.
- Could also decide when to stop if certain success criteria are met (not fully illustrated here).
Run Example
- The if __name__ == "__main__": block demonstrates how to call SupervisorAgent in a loop, simulating multiple rounds of iteration. Each iteration prints a partial meta-review, top hypotheses, and their updated Elo scores.

Adapting for a Real System

LLM Integration:
Replace the placeholder calls (call_llm_for_generation, call_llm_for_reflection, etc.) with actual API calls to Gemini 2.0 or any frontier LLM. Build prompts that incorporate the entire chain-of-thought, the context memory, the research goal, and so on.
Tool Use:
Integrate specialized retrieval or web-search calls to ground the Reflection agent’s analysis in real data and references (PubMed, PatentDB, etc.).
Advanced Tournaments:
You may switch from naive pairwise comparisons to more sophisticated tournament designs, e.g., partial or dynamic bracket systems to handle many hypotheses.
Complex Evolution:
The Evolution agent could adopt multiple strategies (e.g., rewriting text, adding clarifying experiments, splitting hypotheses into subparts) rather than a simple “merge function.”
Database / Vector Storage:
For large volumes of text, embed each hypothesis using sentence transformers or specialized LLM calls and store embeddings in a database. The Proximity agent can then do real nearest-neighbor searches.
User Interface:
Provide a front-end (CLI, web server) for scientists to add feedback dynamically. In a production environment, scientists might type commands or ask new queries that the Supervisor would incorporate mid-cycle.

Conclusion

This Python program demonstrates the structure of a multi-agent “co-scientist” system rather than offering a finished, fully integrated solution. You can use it as a template:

Keep the classes in separate files or modules for maintainability.
Implement concurrency or a queue-based architecture if you need true parallelism.
Enhance each placeholder function with real LLM calls and real domain knowledge.

With these adaptations, you can approximate the AI co-scientist functionality described in the paper—collaboratively generating, debating, refining, and ranking scientific hypotheses to assist human researchers in new discoveries.

Author

chunhualiao commented Feb 22, 2025

o3-mini-high

Below is a complete Python program that uses FastAPI to expose a production‐ready web service for a co‑scientist system. In this implementation, each agent is defined as a class with clearly separated responsibilities, a shared context memory stores hypotheses and logs, and the Supervisor agent coordinates the iterative process. In a production system you would replace the placeholder LLM calls with real API calls and integrate with your persistence layer. You can run this program with Uvicorn.

import math
import random
import logging
from typing import List, Dict, Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn

# Configure logging for production readiness.
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
)
logger = logging.getLogger("co_scientist")

###############################################################################
# Data Models and Pydantic Schemas
###############################################################################

class Hypothesis:
    def __init__(self, hypothesis_id: str, title: str, text: str):
        self.hypothesis_id = hypothesis_id
        self.title = title
        self.text = text
        self.novelty_review: Optional[str] = None   # "HIGH", "MEDIUM", "LOW"
        self.feasibility_review: Optional[str] = None
        self.elo_score: float = 1200.0      # initial Elo score
        self.review_comments: List[str] = []
        self.references: List[str] = []
        self.is_active: bool = True

    def to_dict(self) -> dict:
        return {
            "id": self.hypothesis_id,
            "title": self.title,
            "text": self.text,
            "novelty_review": self.novelty_review,
            "feasibility_review": self.feasibility_review,
            "elo_score": self.elo_score,
            "review_comments": self.review_comments,
            "references": self.references,
            "is_active": self.is_active,
        }

class ResearchGoal:
    def __init__(self, description: str, constraints: Dict = None):
        self.description = description
        self.constraints = constraints if constraints else {}

class ContextMemory:
    """
    A simple in-memory context storage.
    """
    def __init__(self):
        self.hypotheses: Dict[str, Hypothesis] = {}  # key: hypothesis_id
        self.tournament_results: List[Dict] = []
        self.meta_review_feedback: List[Dict] = []
        self.iteration_number: int = 0

    def add_hypothesis(self, hypothesis: Hypothesis):
        self.hypotheses[hypothesis.hypothesis_id] = hypothesis
        logger.info(f"Added hypothesis {hypothesis.hypothesis_id}")

    def get_active_hypotheses(self) -> List[Hypothesis]:
        return [h for h in self.hypotheses.values() if h.is_active]


# Pydantic schemas for API endpoints.
class ResearchGoalRequest(BaseModel):
    description: str
    constraints: Optional[Dict] = {}

class HypothesisResponse(BaseModel):
    id: str
    title: str
    text: str
    novelty_review: Optional[str]
    feasibility_review: Optional[str]
    elo_score: float
    review_comments: List[str]
    references: List[str]
    is_active: bool

class OverviewResponse(BaseModel):
    iteration: int
    meta_review_critique: List[str]
    top_hypotheses: List[HypothesisResponse]
    suggested_next_steps: List[str]


###############################################################################
# Utility Functions (Placeholders for LLM Calls and Similarity Measures)
###############################################################################

def generate_unique_id(prefix="H") -> str:
    return f"{prefix}{random.randint(1000, 9999)}"

def call_llm_for_generation(prompt: str) -> List[Dict]:
    """
    In production, replace this with an actual LLM API call.
    """
    logger.info("LLM generation called with prompt: %s", prompt)
    return [
        {
            "title": "Potential link between epigenetic factor X and disease Y",
            "text": "We hypothesize that factor X modulates gene expression leading to disease Y."
        },
        {
            "title": "Alternative synergy with drug A for condition B",
            "text": "We propose that drug A, originally approved for condition M, might be repurposed for condition B."
        }
    ]

def call_llm_for_reflection(hypothesis_text: str) -> Dict:
    """
    In production, replace this with an actual LLM-based review call.
    """
    outcomes = ["HIGH", "MEDIUM", "LOW"]
    novelty = random.choice(outcomes)
    feasibility = random.choice(outcomes)
    comment = f"Review: novelty={novelty}, feasibility={feasibility}"
    logger.info("LLM reflection for hypothesis: %s", hypothesis_text)
    return {
        "novelty_review": novelty,
        "feasibility_review": feasibility,
        "comment": comment,
        "references": ["PMID:123456", "PMID:789012"]
    }

def run_pairwise_debate(hypoA: Hypothesis, hypoB: Hypothesis) -> Hypothesis:
    """
    Naively compare two hypotheses based on review scores.
    """
    def score(h: Hypothesis) -> int:
        mapping = {"HIGH": 3, "MEDIUM": 2, "LOW": 1, None: 0}
        return mapping.get(h.novelty_review, 0) + mapping.get(h.feasibility_review, 0)
    scoreA = score(hypoA)
    scoreB = score(hypoB)
    winner = hypoA if scoreA > scoreB else hypoB if scoreB > scoreA else random.choice([hypoA, hypoB])
    logger.info("Debate: %s (score %d) vs %s (score %d) => Winner: %s",
                hypoA.hypothesis_id, scoreA, hypoB.hypothesis_id, scoreB, winner.hypothesis_id)
    return winner

def update_elo(winner: Hypothesis, loser: Hypothesis, k_factor: int = 32):
    ratingA = winner.elo_score
    ratingB = loser.elo_score
    expectedA = 1 / (1 + math.pow(10, (ratingB - ratingA) / 400))
    expectedB = 1 - expectedA
    winner.elo_score = ratingA + k_factor * (1 - expectedA)
    loser.elo_score = ratingB + k_factor * (0 - expectedB)
    logger.info("Updated Elo: Winner %s -> %.2f, Loser %s -> %.2f",
                winner.hypothesis_id, winner.elo_score, loser.hypothesis_id, loser.elo_score)

def combine_hypotheses(hypoA: Hypothesis, hypoB: Hypothesis) -> Hypothesis:
    new_id = generate_unique_id("E")
    combined_title = f"Combined: {hypoA.title} & {hypoB.title}"
    combined_text = f"{hypoA.text}\n\nAdditionally, {hypoB.text}"
    logger.info("Combined hypotheses %s and %s into %s", hypoA.hypothesis_id, hypoB.hypothesis_id, new_id)
    return Hypothesis(new_id, combined_title, combined_text)

def similarity_score(textA: str, textB: str) -> float:
    return random.uniform(0, 1)


###############################################################################
# Agent Implementations
###############################################################################

class GenerationAgent:
    def generate_new_hypotheses(self, research_goal: ResearchGoal, context: ContextMemory) -> List[Hypothesis]:
        prompt = (
            f"Research Goal: {research_goal.description}\n"
            f"Constraints: {research_goal.constraints}\n"
            "Please propose 2 new hypotheses with rationale.\n"
        )
        raw_output = call_llm_for_generation(prompt)
        new_hypos = []
        for idea in raw_output:
            hypo_id = generate_unique_id("G")
            h = Hypothesis(hypo_id, idea["title"], idea["text"])
            new_hypos.append(h)
        return new_hypos

class ReflectionAgent:
    def review_hypotheses(self, hypotheses: List[Hypothesis], context: ContextMemory) -> None:
        for h in hypotheses:
            result = call_llm_for_reflection(h.text)
            h.novelty_review = result["novelty_review"]
            h.feasibility_review = result["feasibility_review"]
            h.review_comments.append(result["comment"])
            h.references.extend(result["references"])

class RankingAgent:
    def run_tournament(self, hypotheses: List[Hypothesis], context: ContextMemory) -> None:
        random.shuffle(hypotheses)
        pairs = []
        for i in range(len(hypotheses)):
            for j in range(i + 1, len(hypotheses)):
                pairs.append((hypotheses[i], hypotheses[j]))
        for hA, hB in pairs:
            if hA.is_active and hB.is_active:
                winner = run_pairwise_debate(hA, hB)
                loser = hB if winner == hA else hA
                update_elo(winner, loser)
                context.tournament_results.append({
                    "winner": winner.hypothesis_id,
                    "loser": loser.hypothesis_id,
                    "winner_score": winner.elo_score,
                    "loser_score": loser.elo_score
                })

class EvolutionAgent:
    def evolve_hypotheses(self, top_k: int, context: ContextMemory) -> List[Hypothesis]:
        active = context.get_active_hypotheses()
        sorted_by_elo = sorted(active, key=lambda h: h.elo_score, reverse=True)
        top_candidates = sorted_by_elo[:top_k]
        new_hypotheses = []
        if len(top_candidates) >= 2:
            new_h = combine_hypotheses(top_candidates[0], top_candidates[1])
            new_hypotheses.append(new_h)
        return new_hypotheses

class ProximityAgent:
    def build_proximity_graph(self, hypotheses: List[Hypothesis], context: ContextMemory) -> Dict:
        adjacency = {}
        for i in range(len(hypotheses)):
            adjacency[hypotheses[i].hypothesis_id] = []
            for j in range(len(hypotheses)):
                if i == j:
                    continue
                sim = similarity_score(hypotheses[i].text, hypotheses[j].text)
                adjacency[hypotheses[i].hypothesis_id].append({
                    "other_id": hypotheses[j].hypothesis_id,
                    "similarity": sim
                })
        return adjacency

class MetaReviewAgent:
    def summarize_and_feedback(self, context: ContextMemory, adjacency: Dict) -> Dict:
        reflection_comments = []
        for h in context.get_active_hypotheses():
            reflection_comments.extend(h.review_comments)
        comment_summary = set()
        for c in reflection_comments:
            if "novelty=LOW" in c:
                comment_summary.add("Some ideas are not very novel.")
            if "feasibility=LOW" in c:
                comment_summary.add("Some ideas may be infeasible.")
        best_hypotheses = sorted(context.get_active_hypotheses(), key=lambda h: h.elo_score, reverse=True)[:3]
        overview = {
            "meta_review_critique": list(comment_summary),
            "research_overview": {
                "top_ranked_hypotheses": [h.to_dict() for h in best_hypotheses],
                "suggested_next_steps": [
                    "Conduct further in vitro experiments on top hypotheses.",
                    "Collect domain expert feedback and refine constraints."
                ]
            }
        }
        context.meta_review_feedback.append(overview)
        return overview

class SupervisorAgent:
    def __init__(self):
        self.generation_agent = GenerationAgent()
        self.reflection_agent = ReflectionAgent()
        self.ranking_agent = RankingAgent()
        self.evolution_agent = EvolutionAgent()
        self.proximity_agent = ProximityAgent()
        self.meta_review_agent = MetaReviewAgent()

    def run_cycle(self, research_goal: ResearchGoal, context: ContextMemory) -> Dict:
        logger.info("Starting a new cycle, iteration %d", context.iteration_number + 1)
        # 1. Generation
        new_hypotheses = self.generation_agent.generate_new_hypotheses(research_goal, context)
        for nh in new_hypotheses:
            context.add_hypothesis(nh)
        # 2. Reflection
        active_hypos = context.get_active_hypotheses()
        self.reflection_agent.review_hypotheses(active_hypos, context)
        # 3. Ranking (Tournament)
        active_hypos = context.get_active_hypotheses()
        self.ranking_agent.run_tournament(active_hypos, context)
        # 4. Evolution (Improve top ideas)
        new_evolved = self.evolution_agent.evolve_hypotheses(top_k=2, context=context)
        for nh in new_evolved:
            context.add_hypothesis(nh)
        if new_evolved:
            self.reflection_agent.review_hypotheses(new_evolved, context)
        # 5. Ranking again
        active_hypos = context.get_active_hypotheses()
        self.ranking_agent.run_tournament(active_hypos, context)
        # 6. Proximity Analysis
        adjacency = self.proximity_agent.build_proximity_graph(active_hypos, context)
        # 7. Meta-review
        overview = self.meta_review_agent.summarize_and_feedback(context, adjacency)
        context.iteration_number += 1
        logger.info("Cycle complete, iteration now %d", context.iteration_number)
        return overview

###############################################################################
# FastAPI Application
###############################################################################

app = FastAPI(title="AI Co-Scientist System", version="1.0")

# Global context and supervisor (in production, consider persistent storage)
global_context = ContextMemory()
supervisor = SupervisorAgent()
current_research_goal: Optional[ResearchGoal] = None

@app.post("/research_goal", response_model=dict)
def set_research_goal(goal: ResearchGoalRequest):
    global current_research_goal, global_context
    current_research_goal = ResearchGoal(goal.description, goal.constraints)
    # Reset context for new research goal
    global_context = ContextMemory()
    logger.info("Research goal set: %s", goal.description)
    return {"message": "Research goal successfully set."}

@app.post("/run_cycle", response_model=OverviewResponse)
def run_cycle():
    global current_research_goal, global_context
    if not current_research_goal:
        raise HTTPException(status_code=400, detail="No research goal set.")
    overview_dict = supervisor.run_cycle(current_research_goal, global_context)
    # Build response using best hypotheses from meta review
    top_hypotheses = overview_dict["research_overview"]["top_ranked_hypotheses"]
    response = OverviewResponse(
        iteration=global_context.iteration_number,
        meta_review_critique=overview_dict["meta_review_critique"],
        top_hypotheses=[HypothesisResponse(**h) for h in top_hypotheses],
        suggested_next_steps=overview_dict["research_overview"]["suggested_next_steps"]
    )
    return response

@app.get("/hypotheses", response_model=List[HypothesisResponse])
def list_hypotheses():
    global global_context
    return [HypothesisResponse(**h.to_dict()) for h in global_context.get_active_hypotheses()]

@app.get("/")
def root():
    return {"message": "Welcome to the AI Co-Scientist System. Set your research goal and run cycles to generate hypotheses."}

###############################################################################
# Main Entrypoint
###############################################################################

if __name__ == "__main__":
    # Run with: uvicorn this_script:app --host 0.0.0.0 --port 8000
    uvicorn.run("co_scientist:app", host="0.0.0.0", port=8000, reload=False)

How It Works

Setting the Research Goal:
– POST to /research_goal with a JSON payload (description and optional constraints).
– This resets the context and sets a new research goal.
Running a Cycle:
– POST to /run_cycle initiates one full iteration: generation, reflection, ranking, evolution, proximity analysis, and meta-review.
– The response includes the iteration number, meta-review comments, top hypotheses (with their details), and suggested next steps.
Listing Hypotheses:
– GET /hypotheses returns all active hypotheses stored in the context.
Production Features:
– Logging is configured for traceability.
– Pydantic models ensure data validation.
– The code is structured modularly for future extension (e.g., adding real LLM API calls, asynchronous processing, persistent storage).

This program provides a production-ready template for implementing a multi-agent AI co‑scientist system. You can deploy it with Uvicorn (or another ASGI server) and integrate it with real LLM backends and databases as needed.

Author

chunhualiao commented Feb 22, 2025 •

edited

Loading

Following the design of the uploaded paper, write a complete, production ready python program implementing the agents and their interactions to help scientist users

grok-3

https://docs.google.com/document/d/103Z1JNLx_b1gw3Huwl_0LRfB_QGUzw4XM3Ysg6kJQXU/edit?usp=sharing

To address the user's query, we need to create a complete, production-ready Python program that implements the AI co-scientist system as described in the uploaded document "Towards an AI co-scientist." This system is a multi-agent architecture built on Gemini 2.0, designed to assist scientists in generating novel research hypotheses through a generate, debate, and evolve approach within a tournament framework. It includes specialized agents—Generation, Reflection, Ranking, Evolution, Proximity, and Meta-review—working collaboratively to produce and refine hypotheses based on user-provided research objectives and feedback. Below is the implementation, structured to reflect the system's design and functionality.

Overview of the AI Co-Scientist System

The AI co-scientist system is intended to:

Generate: Produce initial hypotheses based on a research goal and preferences.
Debate: Refine hypotheses through simulated scientific discussions among agents.
Rank: Compare and rank hypotheses using a tournament process.
Evolve: Improve hypotheses by incorporating user feedback and agent critiques.
Analyze: Use Proximity and Meta-review agents for relatedness assessment and high-level synthesis.

The program includes a command-line interface for user interaction, simulates Gemini 2.0 with a mock response function (due to lack of API access), and follows an iterative process where hypotheses are continuously refined based on user input.

Python Implementation

python

import sys  
from typing import List, Dict, Optional

class Agent:  
    """Base class for all agents, handling prompt formatting and response generation."""  
    def \_\_init\_\_(self, prompt\_template: str):  
        self.prompt\_template \= prompt\_template

    def generate\_response(self, \*\*kwargs) \-\> str:  
        """Generate a response using the prompt template and provided arguments."""  
        try:  
            prompt \= self.prompt\_template.format(\*\*kwargs)  
            return generate\_model\_response(prompt)  
        except KeyError as e:  
            raise ValueError(f"Missing required prompt parameter: {e}")

def generate\_model\_response(prompt: str) \-\> str:  
    """  
    Mock function to simulate Gemini 2.0 responses.  
    In production, replace with actual API call to Gemini 2.0.  
    """  
    \# For demonstration, return a simplified response  
    if "generate a novel hypothesis" in prompt.lower():  
        return f"Hypothesis: \[Mock hypothesis based on {prompt\[:50\]}...\]"  
    elif "refined hypothesis:" in prompt.lower():  
        return f"Refined hypothesis: \[Refined mock hypothesis from {prompt\[:50\]}...\]"  
    elif "better hypothesis:" in prompt.lower():  
        return "Rationale: Mock evaluation. better hypothesis: 1"  
    elif "similarity assessment:" in prompt.lower():  
        return "Similarity assessment: Hypotheses are moderately related."  
    elif "meta-review:" in prompt.lower():  
        return "Meta-review: Synthesized insights from reviews."  
    else:  
        return f"Mock response to: {prompt\[:50\]}..."

def conduct\_debate(initial\_hypothesis: str, num\_agents: int \= 3, num\_turns: int \= 5\) \-\> str:  
    """Simulate a scientific debate to refine an initial hypothesis."""  
    debate\_agent \= Agent(prompt\_template="""You are participating in a scientific debate. The conversation so far is: {conversation}. Contribute by critiquing, questioning, or proposing improvements to the hypothesis. End your response with 'Refined hypothesis: \[your refined hypothesis\]'.""")  
    conversation \= \[f"Initial hypothesis: {initial\_hypothesis}"\]  
      
    for turn in range(num\_turns):  
        for agent\_id in range(num\_agents):  
            context \= "\\n".join(conversation)  
            response \= debate\_agent.generate\_response(conversation=context)  
            conversation.append(f"Agent {agent\_id}: {response}")  
      
    \# Extract the final hypothesis from the last response  
    last\_response \= conversation\[-1\]  
    try:  
        final\_hyp \= last\_response.split("Refined hypothesis:")\[-1\].strip()  
        return final\_hyp  
    except IndexError:  
        raise ValueError("Failed to extract refined hypothesis from debate.")

def generate\_initial\_hypotheses(goal: str, preferences: str, num\_hyp: int) \-\> List\[str\]:  
    """Generate initial hypotheses based on research goal and preferences."""  
    generation\_agent \= Agent(prompt\_template="""You are an expert scientist. Based on the following research goal and preferences, generate a novel hypothesis.

Research goal: {goal}  
Preferences: {preferences}  
Hypothesis:""")  
    return \[generation\_agent.generate\_response(goal=goal, preferences=preferences) for \_ in range(num\_hyp)\]

def rank\_hypotheses(hypotheses: List\[str\], criteria: str) \-\> List\[str\]:  
    """Rank hypotheses using pairwise comparisons in a tournament."""  
    ranking\_agent \= Agent(prompt\_template="""You are an expert evaluator. Compare the following two hypotheses based on the specified criteria and determine which one is superior. Provide a concise rationale for your selection, concluding with "better hypothesis: \<1 or 2\>".

Hypothesis 1: {hyp1}  
Hypothesis 2: {hyp2}  
Criteria: {criteria}""")  
    scores \= {hyp: 0 for hyp in hypotheses}  
      
    for i in range(len(hypotheses)):  
        for j in range(i \+ 1, len(hypotheses)):  
            hyp1, hyp2 \= hypotheses\[i\], hypotheses\[j\]  
            response \= ranking\_agent.generate\_response(hyp1=hyp1, hyp2=hyp2, criteria=criteria)  
            if "better hypothesis: 1" in response.lower():  
                scores\[hyp1\] \+= 1  
            elif "better hypothesis: 2" in response.lower():  
                scores\[hyp2\] \+= 1  
      
    return sorted(hypotheses, key=lambda hyp: scores\[hyp\], reverse=True)

def evolve\_hypotheses(hypotheses: List\[str\], feedback: Dict\[str, str\]) \-\> List\[str\]:  
    """Evolve hypotheses based on user feedback."""  
    evolution\_agent \= Agent(prompt\_template="""You are an expert in refining scientific hypotheses. Based on the following hypothesis and feedback, generate an improved version.

Hypothesis: {hypothesis}  
Feedback: {feedback}  
Improved hypothesis:""")  
    return \[evolution\_agent.generate\_response(hypothesis=hyp, feedback=feedback\[hyp\]) for hyp in hypotheses\]

def assess\_proximity(hypotheses: List\[str\]) \-\> List\[str\]:  
    """Assess relatedness of hypotheses using the Proximity agent."""  
    proximity\_agent \= Agent(prompt\_template="""You are an expert in assessing hypothesis relatedness. Determine how similar the following two hypotheses are and whether they address the same underlying idea.

Hypothesis 1: {hyp1}  
Hypothesis 2: {hyp2}  
Similarity assessment:""")  
    assessments \= \[\]  
    for i in range(len(hypotheses)):  
        for j in range(i \+ 1, len(hypotheses)):  
            hyp1, hyp2 \= hypotheses\[i\], hypotheses\[j\]  
            assessment \= proximity\_agent.generate\_response(hyp1=hyp1, hyp2=hyp2)  
            assessments.append(assessment)  
    return assessments

def generate\_meta\_review(hypotheses: List\[str\], reviews: List\[str\]) \-\> str:  
    """Generate a high-level meta-review of hypotheses and their reviews."""  
    meta\_review\_agent \= Agent(prompt\_template="""You are an expert in scientific meta-analysis. Synthesize a comprehensive meta-review of the provided hypotheses and reviews.

Hypotheses: {hypotheses}  
Reviews: {reviews}  
Meta-review:""")  
    return meta\_review\_agent.generate\_response(hypotheses="\\n".join(hypotheses), reviews="\\n".join(reviews))

def get\_user\_feedback(hypotheses: List\[str\]) \-\> Dict\[str, str\]:  
    """Collect feedback from the user for each hypothesis."""  
    feedback \= {}  
    for hyp in hypotheses:  
        print(f"\\nHypothesis: {hyp}")  
        fb \= input("Provide feedback: ")  
        feedback\[hyp\] \= fb or "No feedback provided."  
    return feedback

def main():  
    """Main function to run the AI co-scientist system."""  
    print("Welcome to the AI Co-Scientist System")  
    goal \= input("Enter your research goal: ").strip()  
    preferences \= input("Enter any preferences or constraints: ").strip()  
    criteria \= input("Enter the criteria for evaluating hypotheses: ").strip()  
      
    if not goal or not criteria:  
        print("Error: Research goal and evaluation criteria are required.")  
        sys.exit(1)  
      
    num\_hyp \= 3  \# Number of hypotheses to maintain  
    feedback: Dict\[str, str\] \= {}  
    iteration \= 0  
      
    while True:  
        iteration \+= 1  
        print(f"\\n--- Iteration {iteration} \---")  
          
        \# Step 1: Generate or evolve hypotheses  
        if iteration \== 1:  
            current\_hypotheses \= generate\_initial\_hypotheses(goal, preferences, num\_hyp)  
        else:  
            current\_hypotheses \= evolve\_hypotheses(top\_hypotheses, feedback)  
          
        \# Step 2: Refine hypotheses through debate  
        refined\_hypotheses \= \[conduct\_debate(hyp) for hyp in current\_hypotheses\]  
          
        \# Step 3: Rank hypotheses  
        ranked\_hypotheses \= rank\_hypotheses(refined\_hypotheses, criteria)  
        top\_hypotheses \= ranked\_hypotheses\[:num\_hyp\]  
          
        \# Step 4: Assess proximity (optional analysis)  
        proximity\_assessments \= assess\_proximity(top\_hypotheses)  
        print("\\nProximity Assessments:")  
        for assessment in proximity\_assessments:  
            print(assessment)  
          
        \# Step 5: Collect user feedback  
        feedback \= get\_user\_feedback(top\_hypotheses)  
          
        \# Step 6: Generate meta-review (optional analysis)  
        meta\_review \= generate\_meta\_review(top\_hypotheses, list(feedback.values()))  
        print(f"\\nMeta-review: {meta\_review}")  
          
        \# Step 7: Check if user wants to continue  
        cont \= input("\\nDo you want to continue to the next iteration? (yes/no): ").strip().lower()  
        if cont \!= "yes":  
            break  
      
    print("\\nFinal Top Hypotheses:")  
    for i, hyp in enumerate(top\_hypotheses, 1):  
        print(f"{i}. {hyp}")

if \_\_name\_\_ \== "\_\_main\_\_":  
    try:  
        main()  
    except KeyboardInterrupt:  
        print("\\nProgram terminated by user.")  
    except Exception as e:  
        print(f"Error: {e}")  
        sys.exit(1)

Explanation of the Implementation

Key Components

Agent Class
- A generic base class that formats prompts and generates responses using a mock generate_model_response function.
- In a production environment, replace generate_model_response with an API call to Gemini 2.0.
Specialized Agents
- Generation: Creates initial hypotheses using the research goal and preferences.
- Debate: Simulates a multi-agent discussion to refine hypotheses, with each agent contributing critiques or improvements.
- Ranking: Performs pairwise comparisons in a tournament to rank hypotheses based on user-defined criteria.
- Evolution: Refines hypotheses by incorporating user feedback.
- Proximity: Assesses the relatedness of hypotheses to ensure diversity.
- Meta-review: Synthesizes feedback and provides high-level analysis.
Core Functions
- conduct_debate: Simulates a 3-agent, 5-turn debate to refine a hypothesis, extracting the final version from the last response.
- generate_initial_hypotheses: Produces initial hypotheses for the first iteration.
- rank_hypotheses: Implements a tournament by scoring hypotheses based on wins in pairwise comparisons.
- evolve_hypotheses: Generates improved hypotheses using feedback.
- assess_proximity: Evaluates similarity between hypotheses.
- generate_meta_review: Provides a synthesized overview of hypotheses and feedback.
- get_user_feedback: Collects user input via the command-line interface.
Main Loop
- Iteration 1: Generates initial hypotheses, refines them via debate, ranks them, and collects feedback.
- Subsequent Iterations: Evolves the top hypotheses based on feedback, refines them further, and repeats the process.
- Users can terminate the process at any iteration.

Design Decisions

Mock Model: Since Gemini 2.0 access is unavailable, a mock response function simulates agent behavior. In production, integrate with the actual API.
Fixed Pool Size: Maintains 3 hypotheses per iteration to manage computational complexity, adjustable via num_hyp.
Error Handling: Includes basic try-except blocks to handle user interrupts and prompt formatting errors.
User Interaction: Uses a simple command-line interface for accessibility; a GUI could be added for production use.
Extensibility: The system omits web search and specialized tools (e.g., AlphaFold) due to constraints but is designed to allow future integration.

Alignment with the Paper

Reflects the multi-agent architecture with Generation, Reflection (implicit in debate), Ranking, Evolution, Proximity, and Meta-review agents.
Implements the "generate, debate, and evolve" approach with a tournament framework.
Supports scientist-in-the-loop collaboration via natural language feedback.

Running the Program

Save the code to a file, e.g., ai_co_scientist.py.
Run it with Python 3.x: python ai_co_scientist.py.
Interact:
- Enter a research goal (e.g., "Identify novel treatments for colon cancer").
- Provide preferences (e.g., "Focus on drug repurposing").
- Specify criteria (e.g., "Novelty, feasibility").
- Review hypotheses, provide feedback, and decide whether to continue each iteration.

Production Enhancements

For a fully production-ready system:

API Integration: Replace generate_model_response with Gemini 2.0 API calls.
Robust Parsing: Add logic to handle varied model response formats.
Tool Support: Integrate web search and specialized models (e.g., AlphaFold) as described in the paper.
State Management: Save and load hypotheses and feedback to/from a file or database.
Scalability: Optimize tournament comparisons (e.g., caching) and parallelize debates.
Logging: Add detailed logs for debugging and auditing.

This implementation provides a functional prototype that mirrors the AI co-scientist’s design, ready for enhancement with real-world integrations.

Author

chunhualiao commented Feb 22, 2025

Following the design of the uploaded paper, write a complete, production ready python program implementing the agents and their interactions to help scientist users:

Gemini 2.0 Flash Thinking Experimental 01-21

import asyncio
import time
import logging

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# --- Memory Component ---
class Memory:
    """Simple in-memory storage for agent context."""
    def __init__(self):
        self.data = {}

    def store(self, key, data):
        self.data[key] = data
        logging.debug(f"Memory Stored: {key}")

    def retrieve(self, key):
        data = self.data.get(key)
        logging.debug(f"Memory Retrieved: {key}")
        return data

# --- Tool Use Abstraction ---
class ToolUse:
    """Placeholder for tool use functionalities like search and specialized AI models."""
    def __init__(self):
        pass

    async def search_literature(self, query):
        """Simulate literature search."""
        logging.info(f"Tool Use: Searching literature for: '{query}'")
        await asyncio.sleep(1) # Simulate search delay
        # In a real implementation, this would interact with a search API
        return f"Search results for: {query} (simulated)"

    async def use_specialized_ai_model(self, model_name, input_data):
        """Simulate using a specialized AI model (e.g., AlphaFold)."""
        logging.info(f"Tool Use: Using specialized model: '{model_name}' with input: '{input_data}'")
        await asyncio.sleep(2) # Simulate model processing delay
        # In a real implementation, this would interact with a specialized AI model API
        return f"Model '{model_name}' output for input '{input_data}' (simulated)"


# --- Agent Implementations ---
class GenerationAgent:
    """Agent responsible for generating initial hypotheses and research proposals."""
    def __init__(self, memory, tool_use):
        self.memory = memory
        self.tool_use = tool_use

    async def generate_hypotheses(self, research_goal):
        logging.info("Generation Agent: Generating initial hypotheses...")
        # Simulate literature exploration
        literature_summary = await self.tool_use.search_literature(f"background information for: {research_goal}")
        logging.info(f"Generation Agent: Literature Summary: {literature_summary}")

        # Simulate hypothesis generation based on goal and literature
        hypotheses = [
            f"Hypothesis 1: Novel mechanism related to {research_goal} (Generated by Generation Agent - Iteration 1)",
            f"Hypothesis 2: Alternative approach to {research_goal} (Generated by Generation Agent - Iteration 1)",
            f"Hypothesis 3: Combining existing ideas for {research_goal} (Generated by Generation Agent - Iteration 1)"
        ]
        self.memory.store("hypotheses", hypotheses) # Store in memory for other agents
        logging.info(f"Generation Agent: Generated hypotheses: {hypotheses}")
        return hypotheses

class ReflectionAgent:
    """Agent simulating peer review, evaluating hypotheses."""
    def __init__(self, memory, tool_use):
        self.memory = memory
        self.tool_use = tool_use

    async def review_hypotheses(self):
        hypotheses = self.memory.retrieve("hypotheses")
        if not hypotheses:
            logging.warning("Reflection Agent: No hypotheses to review.")
            return []

        logging.info("Reflection Agent: Reviewing hypotheses...")
        reviewed_hypotheses = []
        for hypothesis in hypotheses:
            # Simulate review process (e.g., checking novelty, plausibility)
            review_feedback = f"Review for: '{hypothesis}' - (simulated review feedback, e.g., novelty check, feasibility concerns)"
            logging.info(f"Reflection Agent: {review_feedback}")
            reviewed_hypotheses.append({"hypothesis": hypothesis, "review": review_feedback})

        self.memory.store("reviewed_hypotheses", reviewed_hypotheses) # Store reviewed hypotheses
        logging.info("Reflection Agent: Hypothesis review complete.")
        return reviewed_hypotheses

class RankingAgent:
    """Agent for ranking hypotheses through simulated scientific debate."""
    def __init__(self, memory):
        self.memory = memory

    async def rank_hypotheses(self):
        reviewed_hypotheses = self.memory.retrieve("reviewed_hypotheses")
        if not reviewed_hypotheses:
            logging.warning("Ranking Agent: No reviewed hypotheses to rank.")
            return []

        logging.info("Ranking Agent: Ranking hypotheses...")
        # Simulate tournament or ranking process (e.g., Elo-based, pairwise comparisons)
        ranked_hypotheses = sorted(reviewed_hypotheses, key=lambda x: len(x["review"]), reverse=True) # Simple ranking by review length (placeholder)
        self.memory.store("ranked_hypotheses", ranked_hypotheses) # Store ranked hypotheses
        logging.info("Ranking Agent: Hypothesis ranking complete.")
        return ranked_hypotheses

class EvolutionAgent:
    """Agent for refining and improving top-ranked hypotheses."""
    def __init__(self, memory, tool_use):
        self.memory = memory
        self.tool_use = tool_use

    async def evolve_hypotheses(self):
        ranked_hypotheses = self.memory.retrieve("ranked_hypotheses")
        if not ranked_hypotheses:
            logging.warning("Evolution Agent: No ranked hypotheses to evolve.")
            return []

        top_hypothesis = ranked_hypotheses[0]["hypothesis"] if ranked_hypotheses else None # Take top ranked hypothesis
        if not top_hypothesis:
            return []

        logging.info("Evolution Agent: Evolving top hypothesis...")
        # Simulate evolution/refinement process (e.g., simplification, research extension)
        evolved_hypothesis = f"Refined {top_hypothesis} (Evolved by Evolution Agent - Iteration 1)"
        self.memory.store("evolved_hypothesis", evolved_hypothesis) # Store evolved hypothesis
        logging.info(f"Evolution Agent: Evolved hypothesis: {evolved_hypothesis}")
        return [evolved_hypothesis]  # Return as list for consistency

class ProximityAgent:
    """Agent for calculating similarity and clustering hypotheses (placeholder)."""
    def __init__(self, memory):
        self.memory = memory

    async def compute_proximity(self):
        hypotheses = self.memory.retrieve("hypotheses")
        if not hypotheses:
            logging.warning("Proximity Agent: No hypotheses to compute proximity for.")
            return {}

        logging.info("Proximity Agent: Computing hypothesis proximity (simulated)...")
        # Simulate proximity computation - in real implementation, use NLP techniques
        proximity_graph = {"hypotheses": hypotheses, "proximity_data": "Simulated proximity data"} # Placeholder
        self.memory.store("proximity_graph", proximity_graph)
        logging.info("Proximity Agent: Proximity computation complete (simulated).")
        return proximity_graph

class MetaReviewAgent:
    """Agent for meta-review, synthesizing insights from reviews (placeholder)."""
    def __init__(self, memory):
        self.memory = memory

    async def generate_meta_review(self):
        reviewed_hypotheses = self.memory.retrieve("reviewed_hypotheses")
        if not reviewed_hypotheses:
            logging.warning("Meta-review Agent: No reviewed hypotheses for meta-review.")
            return "No meta-review available."

        logging.info("Meta-review Agent: Generating meta-review (simulated)...")
        # Simulate meta-review synthesis - in real implementation, analyze review feedback
        meta_review = "Meta-review summary based on reviews (simulated).  Key recurring issues and potential improvements identified." # Placeholder
        self.memory.store("meta_review", meta_review)
        logging.info("Meta-review Agent: Meta-review generated (simulated).")
        return meta_review

class SupervisorAgent:
    """Orchestrates the AI co-scientist workflow."""
    def __init__(self, memory, tool_use):
        self.memory = memory
        self.tool_use = tool_use
        self.generation_agent = GenerationAgent(memory, tool_use)
        self.reflection_agent = ReflectionAgent(memory, tool_use)
        self.ranking_agent = RankingAgent(memory)
        self.evolution_agent = EvolutionAgent(memory, tool_use)
        self.proximity_agent = ProximityAgent(memory)
        self.meta_review_agent = MetaReviewAgent(memory)

    async def run_workflow(self, research_goal, num_iterations=1):
        logging.info("Supervisor Agent: Starting AI co-scientist workflow...")
        self.memory.store("research_goal", research_goal)

        for iteration in range(num_iterations):
            logging.info(f"Supervisor Agent: --- Iteration {iteration + 1} ---")

            # Generation Phase
            await self.generation_agent.generate_hypotheses(research_goal)

            # Reflection Phase
            await self.reflection_agent.review_hypotheses()

            # Ranking Phase
            await self.ranking_agent.rank_hypotheses()

            # Proximity Analysis (optional, can be run less frequently)
            await self.proximity_agent.compute_proximity()

            # Meta-Review (can be run after multiple iterations of reviews)
            meta_review = await self.meta_review_agent.generate_meta_review()
            logging.info(f"Supervisor Agent: Meta-Review: {meta_review}")
            self.memory.store("meta_review_iteration_"+str(iteration), meta_review)

            # Evolution Phase (refine top hypothesis for next iteration)
            await self.evolution_agent.evolve_hypotheses()
            evolved_hypothesis = self.memory.retrieve("evolved_hypothesis")
            if evolved_hypothesis:
                self.memory.store("hypotheses", evolved_hypothesis) # Replace hypotheses with evolved one (simplified for example)
                logging.info(f"Supervisor Agent: Updated Hypotheses with evolved version for next iteration.")


        ranked_hypotheses = self.memory.retrieve("ranked_hypotheses")
        research_overview = await self._generate_research_overview(ranked_hypotheses)
        return research_overview

    async def _generate_research_overview(self, ranked_hypotheses):
        """Simulate generating a research overview from ranked hypotheses."""
        logging.info("Supervisor Agent: Generating Research Overview...")
        overview_content = "Research Overview:\n"
        if ranked_hypotheses:
            overview_content += "Top Ranked Hypotheses:\n"
            for i, hypothesis_data in enumerate(ranked_hypotheses[:3]): # Show top 3 for overview
                overview_content += f"  {i+1}. {hypothesis_data['hypothesis']} - Review: {hypothesis_data['review']}\n"
        else:
            overview_content += "No hypotheses generated yet.\n"
        overview_content += f"\nMeta-Review Insights (from last iteration): {self.memory.retrieve('meta_review_iteration_'+ str(1 if self.memory.retrieve('meta_review_iteration_1') else 0) ) or 'Not available yet'}\n" # Simplistic meta-review retrieval

        logging.info("Supervisor Agent: Research overview generated.")
        return overview_content


# --- Scientist Interface ---
class ScientistInterface:
    """Handles interaction with the scientist user."""
    def __init__(self, supervisor_agent):
        self.supervisor_agent = supervisor_agent

    async def get_research_goal_from_user(self):
        return input("Scientist: Please enter your research goal in natural language:\n")

    async def display_research_overview(self, overview):
        print("\n--- AI Co-scientist Research Overview ---")
        print(overview)
        print("--- End of Overview ---")

    async def chat_interface(self): # Simple CLI based chat, expandable to GUI or web interface
        print("Welcome to the AI Co-scientist Chat Interface.")
        while True:
            user_input = await self.get_research_goal_from_user()
            if user_input.lower() == 'exit':
                print("Exiting chat interface.")
                break

            overview = await self.supervisor_agent.run_workflow(user_input, num_iterations=2) # Example iterations
            await self.display_research_overview(overview)
            print("\nWhat would you like to explore next? (or type 'exit' to quit)")

# --- Main Execution ---
async def main():
    memory = Memory()
    tool_use = ToolUse()
    supervisor = SupervisorAgent(memory, tool_use)
    scientist_interface = ScientistInterface(supervisor)

    await scientist_interface.chat_interface() # Start the chat interface

if __name__ == "__main__":
    asyncio.run(main())

Explanation and How to Run:

Save the code: Save the code above as a Python file (e.g., ai_coscientist.py).
Install asyncio (if not already installed):
```
pip install asyncio
```
Run the program:
```
python ai_coscientist.py
```
Interaction: The program will start a simple command-line interface:
```
Welcome to the AI Co-scientist Chat Interface.
Scientist: Please enter your research goal in natural language:
```
Enter a research goal (e.g., "Develop a novel drug repurposing candidate for acute myeloid leukemia (AML)" ) and press Enter.
Workflow Execution: The SupervisorAgent will orchestrate the agents through a few iterations (as set in num_iterations=2 in chat_interface()). You'll see logging messages indicating which agent is running and simulated outputs.
Research Overview: After the iterations, the ScientistInterface will display a "Research Overview" summarizing the top-ranked hypotheses and meta-review (simulated).
Continue Chat: You can enter another research goal or type exit to quit.

Code Breakdown:

Logging: Basic logging is included to trace the execution flow and agent activities.
Memory Class: A simple in-memory dictionary to simulate the persistent context memory. In a real system, you might use a database or more robust storage.
ToolUse Class: A placeholder for external tools. search_literature and use_specialized_ai_model are simulated methods. In a production system, these would be replaced with actual API calls to search engines, specialized AI models (like AlphaFold), databases, etc.
Agent Classes (GenerationAgent, ReflectionAgent, etc.):
- Each agent is implemented as a class.
- __init__ initializes the agent with memory and tool_use (where needed).
- run_* methods (or similar) encapsulate the core logic of each agent. Crucially, the core AI logic within these agents is heavily simulated in this example. In a real system, these methods would contain code to interact with LLMs (like Gemini 2.0), perform complex reasoning, etc.
SupervisorAgent: The central orchestrator.
- __init__ creates instances of all the agents.
- run_workflow is the main method that drives the "generate, debate, evolve" cycle. It calls the run_* methods of each agent in sequence.
- _generate_research_overview is a placeholder to create a summary of the results.
ScientistInterface: Handles the user interaction (command-line in this case).
- get_research_goal_from_user gets input from the scientist.
- display_research_overview shows the results.
- chat_interface sets up the main loop for interaction.
main() and if __name__ == "__main__":: Sets up the program and starts the chat_interface.

Important Notes and Next Steps for a Production-Ready System:

LLM Integration: The most critical step is to replace the simulated agent logic with actual interactions with a Large Language Model (like Gemini 2.0). You would use an LLM API (like the Gemini API) to send prompts to the LLM for each agent's task (hypothesis generation, review, ranking, etc.). The prompts would be carefully designed based on the paper's descriptions and examples in the Appendix.
Asynchronous Execution: Implement true asynchronous task execution using asyncio or threading. This would allow agents to run concurrently and improve performance, especially for time-consuming tasks like literature search or specialized AI model usage.
Advanced Tool Use: Replace the simulated ToolUse with real integrations:
- Literature Search: Use a proper search API (e.g., Google Scholar API, PubMed API).
- Specialized AI Models: Integrate with APIs for protein structure prediction (AlphaFold API), cheminformatics tools, databases, etc.
Robust Memory: Use a persistent database (like SQLite, PostgreSQL, or a vector database) for storing context memory, hypotheses, reviews, etc.
Sophisticated Ranking: Implement a more realistic ranking algorithm like Elo-based tournament ranking, as described in the paper.
Advanced NLP and Reasoning in Agents: The agents' logic needs to be significantly enhanced to perform actual scientific reasoning, novelty detection, plausibility checks, etc. This will heavily rely on effective LLM prompting and potentially fine-tuning.
Error Handling and Robustness: Add error handling, logging, and mechanisms to make the system more robust and handle unexpected situations.
User Interface: Develop a more user-friendly interface (GUI or web-based) for scientists to interact with the system.
Safety and Ethical Considerations: Implement safety and ethical safeguards as discussed in the paper.

This Python code provides a solid foundation. Building a true AI co-scientist as described in the paper is a complex and ongoing research project, but this skeleton gives you a starting point to implement the core architecture and agent interactions.

Author

chunhualiao commented Feb 22, 2025

deepseek-r1

I'll create a simplified but architecturally complete implementation reflecting the paper's core design. Note that for brevity, we'll use placeholder LLM calls and focus on the agent interaction patterns.

import asyncio
from typing import List, Dict, Optional
from pydantic import BaseModel
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer

# ---------- Core Models ----------
class Hypothesis(BaseModel):
    id: str
    content: str
    elo: float = 1200
    reviews: List[dict] = []
    parent_ids: List[str] = []
    status: str = "new"

class ResearchGoal(BaseModel):
    description: str
    constraints: Dict[str, str]
    hypotheses: List[Hypothesis] = []

# ---------- Agent Base Class ----------
class Agent:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
    
    async def process_task(self, goal: ResearchGoal, hypothesis: Optional[Hypothesis] = None):
        raise NotImplementedError

# ---------- Specialized Agents ----------
class GenerationAgent(Agent):
    async def process_task(self, goal: ResearchGoal, _=None):
        # Simulated LLM call for hypothesis generation
        hypotheses = await self._generate_hypotheses(goal)
        return [Hypothesis(id=f"gen_{np.random.randint(1000)}", content=h) for h in hypotheses]
    
    async def _generate_hypotheses(self, goal: ResearchGoal) -> List[str]:
        # Placeholder - would integrate with real LLM and web search
        return [
            f"Hypothesis about {goal.description[:20]}... via pathway modulation",
            f"Novel mechanism combining {np.random.choice(['X', 'Y', 'Z'])} factors"
        ]

class ReflectionAgent(Agent):
    async def process_task(self, goal: ResearchGoal, hypothesis: Hypothesis):
        # Simulated validation and review
        review = {
            "novelty": np.random.uniform(0.7, 0.95),
            "feasibility": np.random.uniform(0.5, 0.9),
            "safety": "approved",
            "notes": "Potential gap in validation methodology"
        }
        hypothesis.reviews.append(review)
        return hypothesis

class RankingAgent(Agent):
    def __init__(self):
        super().__init__()
        self.k_factor = 32  # Elo rating system parameter
    
    async def debate(self, hyp_a: Hypothesis, hyp_b: Hypothesis) -> Hypothesis:
        # Simulated pairwise debate outcome
        return hyp_a if np.random.rand() > 0.5 else hyp_b
    
    def update_elo(self, winner: Hypothesis, loser: Hypothesis):
        expected_win = 1 / (1 + 10**((loser.elo - winner.elo)/400))
        winner.elo += self.k_factor * (1 - expected_win)
        loser.elo += self.k_factor * (expected_win - 1)

class EvolutionAgent(Agent):
    async def process_task(self, goal: ResearchGoal, hypothesis: Hypothesis):
        # Generate refined hypothesis
        mutated = f"Refined {hypothesis.content} with additional considerations"
        return Hypothesis(
            id=f"evol_{hypothesis.id}",
            content=mutated,
            parent_ids=[hypothesis.id]
        )

class ProximityAgent(Agent):
    async def calculate_similarity(self, hypotheses: List[Hypothesis]) -> np.ndarray:
        embeddings = self.model.encode([h.content for h in hypotheses])
        return cosine_similarity(embeddings)

# ---------- Orchestration System ----------
class AICoScientist:
    def __init__(self):
        self.agents = {
            "generate": GenerationAgent(),
            "reflect": ReflectionAgent(),
            "rank": RankingAgent(),
            "evolve": EvolutionAgent(),
            "proximity": ProximityAgent()
        }
        self.task_queue = asyncio.Queue()
        self.hypotheses = []
    
    async def add_goal(self, goal: ResearchGoal):
        await self.task_queue.put(("new_goal", goal))
    
    async def process_tasks(self):
        while True:
            task_type, data = await self.task_queue.get()
            if task_type == "new_goal":
                await self._process_new_goal(data)
            elif task_type == "evolve_hypothesis":
                await self._process_evolution(data)
    
    async def _process_new_goal(self, goal: ResearchGoal):
        # Initial hypothesis generation
        gen_agent = self.agents["generate"]
        hypotheses = await gen_agent.process_task(goal)
        
        # Reflection and validation
        refl_agent = self.agents["reflect"]
        validated = [await refl_agent.process_task(goal, h) for h in hypotheses]
        
        self.hypotheses.extend(validated)
        await self._start_tournament()
    
    async def _start_tournament(self):
        rank_agent = self.agents["rank"]
        prox_agent = self.agents["proximity"]
        
        similarity_matrix = await prox_agent.calculate_similarity(self.hypotheses)
        
        # Run pairwise debates
        for i in range(len(self.hypotheses)):
            for j in range(i+1, len(self.hypotheses)):
                if similarity_matrix[i,j] > 0.7:  # Debate similar hypotheses
                    winner = await rank_agent.debate(
                        self.hypotheses[i], 
                        self.hypotheses[j]
                    )
                    rank_agent.update_elo(winner, 
                        self.hypotheses[j] if winner == self.hypotheses[i] 
                        else self.hypotheses[i]
                    )
        
        # Schedule evolution of top hypotheses
        top_hypotheses = sorted(self.hypotheses, key=lambda x: x.elo, reverse=True)[:3]
        for hyp in top_hypotheses:
            await self.task_queue.put(("evolve_hypothesis", hyp))
    
    async def _process_evolution(self, hypothesis: Hypothesis):
        evol_agent = self.agents["evolve"]
        new_hypothesis = await evol_agent.process_task(None, hypothesis)
        self.hypotheses.append(new_hypothesis)
        await self._start_tournament()

# ---------- Example Usage ----------
async def main():
    system = AICoScientist()
    goal = ResearchGoal(
        description="Identify drug repurposing candidates for AML",
        constraints={"max_toxicity": "IC50 < 100nM"}
    )
    
    # Start processing tasks
    processor = asyncio.create_task(system.process_tasks())
    await system.add_goal(goal)
    
    # Let the system run for several iterations
    await asyncio.sleep(5)
    
    # Display results
    print("Top Hypotheses:")
    for hyp in sorted(system.hypotheses, key=lambda x: x.elo, reverse=True)[:5]:
        print(f"[Elo {hyp.elo:.1f}] {hyp.content}")

if __name__ == "__main__":
    asyncio.run(main())

This implementation demonstrates:

Core Architecture:

Asynchronous task processing
Agent specialization with clear responsibilities
Elo-based ranking system
Hypothesis evolution pipeline

Key Features:

Modular agent design for easy extension
Vector similarity calculations for hypothesis clustering
Iterative refinement process
Tournament-based ranking system

Production-Ready Considerations:

Type hints for maintainability
Asynchronous I/O for scalability
Modular components (easy to swap LLM backends)
State management for hypotheses

Paper-Aligned Components:

Generate-Debate-Evolve loop
Multi-agent collaboration
Scientist-in-the-loop via ResearchGoal constraints
Continuous improvement mechanism

To make this fully production ready you would:

Integrate with actual LLM APIs (Gemini/OpenAI)
Add persistent storage (hypotheses, research goals)
Implement real web search/document retrieval
Add authentication/authorization
Build proper logging/monitoring
Create API endpoints for scientist interaction
Implement full safety reviews
Add configuration management
Implement proper error handling
Add test coverage and CI/CD pipeline

This provides the core framework that aligns with the paper's described architecture while maintaining software engineering best practices.

chunhualiao/ai co-scientist.md

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chunhualiao/ai co-scientist.md

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AI Co-Scientist Paper: Key Insights & Know-Hows

1. System Architecture

2. Hypothesis Generation Process

3. Validation & Applications

4. Technical Components

5. Evaluations & Metrics

6. Limitations

7. Safety & Ethics

8. Future Directions

9. Impact on Scientific Research

10. Miscellaneous

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The Birth of the AI Co-Scientist

The Multi-Agent Meeting of Minds

The Scientist-in-the-Loop

A Call from the Oncology Wing

The Tournament of Ideas

A Challenge from the Hepatology Lab

The Ten-Year Shortcut

The Scientific Tapestry

The Road Ahead

Epilogue: A Legacy of Collaboration

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1. Overall System Flow

2. Example Prompts and Roles for Each Agent

2.1 Supervisor Agent

2.2 Generation Agent

2.3 Reflection Agent

2.4 Ranking Agent

2.5 Evolution Agent

2.6 Proximity Agent

2.7 Meta-review Agent

3. Putting It All Together

Conclusion

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High-Level Overview

Explanation of Key Components

Adapting for a Real System

Conclusion

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How It Works

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chunhualiao commented Feb 22, 2025 •

edited

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