ResearchTown enables AI agents to simulate research communities by modeling researchers and papers as nodes in a graph network.

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🤔 Original Problem:

While LLMs show promise in scientific research, simulating entire research communities with multiple agents collaborating on papers and reviews remains unsolved.

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🔧 Solution in this Paper:

→ ResearchTown introduces an agent-data graph framework where researchers are agent nodes and papers are data nodes.

→ A text-based Graph Neural Network (TextGNN) enables message passing between nodes through paper reading, writing and reviewing activities.

→ The framework evaluates research quality through node-masking prediction tasks, comparing generated content with real papers.

→ ResearchBench benchmark contains 1,000 paper writing and 200 review writing tasks for objective assessment.

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💡 Key Insights:

→ Research communities can be effectively modeled as graphs with researchers and papers as nodes

→ Text-based message passing between nodes enables collaborative research simulation

→ Node masking provides an objective way to evaluate research quality

→ Multiple researchers improve paper quality and review accuracy

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📊 Results:

→ Paper writing similarity score: 0.67 compared to real papers

→ Review writing similarity score: 0.49 for evaluating papers

→ Performance improves with more researchers/reviewers

→ Maintains consistency even with unrelated papers