title: ResearchTown: Simulator of Human Research Community
summary: Large Language Models (LLMs) have demonstrated remarkable potential in
  scientific domains, yet a fundamental question remains unanswered: Can we
  simulate human research communities with LLMs? Addressing this question can
  deepen our understanding of the processes behind idea brainstorming and inspire
  the automatic discovery of novel scientific insights. In this work, we propose
  ResearchTown, a multi-agent framework for research community simulation. Within
  this framework, the human research community is simplified and modeled as an
  agent-data graph, where researchers and papers are represented as agent-type
  and data-type nodes, respectively, and connected based on their collaboration
  relationships. We also introduce TextGNN, a text-based inference framework that
  models various research activities (e.g., paper reading, paper writing, and
  review writing) as special forms of a unified message-passing process on the
  agent-data graph. To evaluate the quality of the research simulation, we
  present ResearchBench, a benchmark that uses a node-masking prediction task for
  scalable and objective assessment based on similarity. Our experiments reveal
  three key findings: (1) ResearchTown can provide a realistic simulation of
  collaborative research activities, including paper writing and review writing;
  (2) ResearchTown can maintain robust simulation with multiple researchers and
  diverse papers; (3) ResearchTown can generate interdisciplinary research ideas
  that potentially inspire novel research directions.
paper_authors._id: 676aaa45d4000ace4575110c
paper_authors.name: Haofei Yu
paper_authors._id: 676aaa45d4000ace4575110d
paper_authors.name: Zhaochen Hong
paper_authors._id: 676aaa45d4000ace4575110e
paper_authors.name: Zirui Cheng
paper_authors._id: 676aaa45d4000ace4575110f
paper_authors.name: Kunlun Zhu
paper_authors._id: 676aaa45d4000ace45751110
paper_authors.name: Keyang Xuan
paper_authors._id: 676aaa45d4000ace45751111
paper_authors.name: Jinwei Yao
paper_authors._id: 676aaa45d4000ace45751112
paper_authors.name: Tao Feng
paper_authors._id: 676aaa45d4000ace45751113
paper_authors.name: Jiaxuan You
paper_title: ResearchTown: Simulator of Human Research Community
paper_summary: Large Language Models (LLMs) have demonstrated remarkable potential in
  scientific domains, yet a fundamental question remains unanswered: Can we
  simulate human research communities with LLMs? Addressing this question can
  deepen our understanding of the processes behind idea brainstorming and inspire
  the automatic discovery of novel scientific insights. In this work, we propose
  ResearchTown, a multi-agent framework for research community simulation. Within
  this framework, the human research community is simplified and modeled as an
  agent-data graph, where researchers and papers are represented as agent-type
  and data-type nodes, respectively, and connected based on their collaboration
  relationships. We also introduce TextGNN, a text-based inference framework that
  models various research activities (e.g., paper reading, paper writing, and
  review writing) as special forms of a unified message-passing process on the
  agent-data graph. To evaluate the quality of the research simulation, we
  present ResearchBench, a benchmark that uses a node-masking prediction task for
  scalable and objective assessment based on similarity. Our experiments reveal
  three key findings: (1) ResearchTown can provide a realistic simulation of
  collaborative research activities, including paper writing and review writing;
  (2) ResearchTown can maintain robust simulation with multiple researchers and
  diverse papers; (3) ResearchTown can generate interdisciplinary research ideas
  that potentially inspire novel research directions.
paper_ai_keywords: multi-agent framework agent-data graph node-masking prediction TextGNN message-passing process