title: OpenResearcher: Unleashing AI for Accelerated Scientific Research
summary: The rapid growth of scientific literature imposes significant challenges for
  researchers endeavoring to stay updated with the latest advancements in their
  fields and delve into new areas. We introduce OpenResearcher, an innovative
  platform that leverages Artificial Intelligence (AI) techniques to accelerate
  the research process by answering diverse questions from researchers.
  OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to
  integrate Large Language Models (LLMs) with up-to-date, domain-specific
  knowledge. Moreover, we develop various tools for OpenResearcher to understand
  researchers' queries, search from the scientific literature, filter retrieved
  information, provide accurate and comprehensive answers, and self-refine these
  answers. OpenResearcher can flexibly use these tools to balance efficiency and
  effectiveness. As a result, OpenResearcher enables researchers to save time and
  increase their potential to discover new insights and drive scientific
  breakthroughs. Demo, video, and code are available at:
  https://github.com/GAIR-NLP/OpenResearcher.
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paper_authors.name: Yuxiang Zheng
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paper_authors.name: Lin Qiu
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paper_authors.name: Cheng Jiayang
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paper_authors.fullname: Jiayang (Joey) Cheng
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paper_authors.name: Xuefeng Li
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paper_authors.name: Jifan Lin
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paper_authors.fullname: Jifan Lin
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paper_authors.user: evanlin2570
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paper_authors.name: Binjie Wang
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paper_authors.fullname: wang
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paper_authors.user: wangbinjie
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paper_authors.name: Yun Luo
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paper_authors.name: Renjie Pan
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paper_authors.name: Yang Xu
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paper_authors.name: Qingkai Min
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paper_authors.name: Zizhao Zhang
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paper_authors.name: Yiwen Wang
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paper_authors.fullname: Yiwen Wang
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paper_authors.name: Wenjie Li
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paper_authors.name: Pengfei Liu
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paper_authors.fullname: Pengfei Liu
paper_authors.type: user
paper_authors.user: Pengfei
paper_title: OpenResearcher: Unleashing AI for Accelerated Scientific Research
paper_summary: The rapid growth of scientific literature imposes significant challenges for
  researchers endeavoring to stay updated with the latest advancements in their
  fields and delve into new areas. We introduce OpenResearcher, an innovative
  platform that leverages Artificial Intelligence (AI) techniques to accelerate
  the research process by answering diverse questions from researchers.
  OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to
  integrate Large Language Models (LLMs) with up-to-date, domain-specific
  knowledge. Moreover, we develop various tools for OpenResearcher to understand
  researchers' queries, search from the scientific literature, filter retrieved
  information, provide accurate and comprehensive answers, and self-refine these
  answers. OpenResearcher can flexibly use these tools to balance efficiency and
  effectiveness. As a result, OpenResearcher enables researchers to save time and
  increase their potential to discover new insights and drive scientific
  breakthroughs. Demo, video, and code are available at:
  https://github.com/GAIR-NLP/OpenResearcher.
paper_ai_summary: OpenResearcher is an AI-driven platform using RAG and LLMs to assist researchers by answering questions and retrieving domain-specific information efficiently and accurately.
paper_ai_keywords: Retrieval-Augmented Generation RAG Large Language Models LLMs