Research Town Guide

# Research Town interaction structure
class ResearchTownSession:
    def __init__(self, agents, topic):
        self.agents = agents
        self.topic = topic
        self.rounds = []

    def run_brainstorming(self, num_rounds=3):
        """Structured brainstorming with multiple agents."""
        ideas = []
        for round_num in range(num_rounds):
            round_ideas = []
            for agent in self.agents:
                # Each agent generates ideas given prior context
                idea = agent.generate_idea(
                    topic=self.topic,
                    prior_ideas=ideas,
                    round=round_num
                )
                round_ideas.append(idea)

            # Cross-pollination: agents react to each other's ideas
            for agent in self.agents:
                reactions = agent.react_to_ideas(round_ideas)
                ideas.extend(reactions)

            self.rounds.append(round_ideas)
        return ideas

    def run_peer_review(self, paper_draft):
        """Simulate peer review with multiple reviewers."""
        reviews = []
        for reviewer in self.agents:
            if reviewer.role == "reviewer":
                review = reviewer.review_paper(
                    paper_draft,
                    criteria=["novelty", "significance",
                             "methodology", "clarity", "reproducibility"]
                )
                reviews.append(review)

        # Meta-review: aggregate and identify consensus
        meta_review = self.aggregate_reviews(reviews)
        return meta_review

# agents.yaml - Agent configuration
agents:
  - name: "Prof. ML Expert"
    role: principal_investigator
    expertise: ["machine learning", "deep learning", "optimization"]
    style: "rigorous, quantitative, focused on scalability"
    publication_venues: ["NeurIPS", "ICML", "JMLR"]
    h_index: 45

  - name: "Dr. Biology Specialist"
    role: domain_expert
    expertise: ["structural biology", "protein engineering", "bioinformatics"]
    style: "experimental, emphasizes biological validity"
    publication_venues: ["Nature", "Cell", "PNAS"]
    h_index: 32

  - name: "Dr. Statistics"
    role: methodologist
    expertise: ["causal inference", "experimental design", "Bayesian methods"]
    style: "rigorous, demands proper statistical justification"
    publication_venues: ["JASA", "Biometrika", "Statistical Science"]
    h_index: 28

  - name: "Reviewer Alpha"
    role: reviewer
    expertise: ["interdisciplinary research", "computational biology"]
    style: "constructive but demanding, focuses on reproducibility"
    review_experience: 200

  - name: "Skeptic"
    role: devils_advocate
    expertise: ["philosophy of science", "replication crisis", "research methods"]
    style: "challenges assumptions, demands strong evidence"

from research_town import ResearchTown, Agent

# Initialize agents from profiles
town = ResearchTown.from_config("agents.yaml")

# Define research topic
topic = {
    "area": "AI for Drug Discovery",
    "question": "How can we improve the efficiency of virtual screening "
                "for novel antibiotics using foundation models?",
    "constraints": [
        "Must work with limited labeled data (<1000 compounds)",
        "Must generalize across bacterial species",
        "Should produce interpretable predictions"
    ]
}

# Run brainstorming (3 rounds of idea generation and critique)
session = town.create_session(topic)
ideas = session.run_brainstorming(num_rounds=3)

# Rank ideas by multi-agent consensus
ranked = session.rank_ideas(ideas, criteria=[
    "novelty",       # How different from existing approaches
    "feasibility",   # Can it be implemented with current resources
    "impact",        # Potential significance if successful
    "rigor"          # Methodological soundness
])

# Output top ideas with supporting analysis
for i, idea in enumerate(ranked[:5]):
    print(f"\n=== Idea {i+1} (Score: {idea.score:.2f}) ===")
    print(f"Title: {idea.title}")
    print(f"Summary: {idea.summary}")
    print(f"Proposed by: {idea.proposer.name}")
    print(f"Endorsed by: {[a.name for a in idea.endorsers]}")
    print(f"Critiques: {idea.critiques}")