title: OmniThink: Expanding Knowledge Boundaries in Machine Writing through
    Thinking
summary: Machine writing with large language models often relies on
  retrieval-augmented generation. However, these approaches remain confined
  within the boundaries of the model's predefined scope, limiting the generation
  of content with rich information. Specifically, vanilla-retrieved information
  tends to lack depth, utility, and suffers from redundancy, which negatively
  impacts the quality of generated articles, leading to shallow, repetitive, and
  unoriginal outputs. To address these issues, we propose OmniThink, a machine
  writing framework that emulates the human-like process of iterative expansion
  and reflection. The core idea behind OmniThink is to simulate the cognitive
  behavior of learners as they progressively deepen their knowledge of the
  topics. Experimental results demonstrate that OmniThink improves the knowledge
  density of generated articles without compromising metrics such as coherence
  and depth. Human evaluations and expert feedback further highlight the
  potential of OmniThink to address real-world challenges in the generation of
  long-form articles.
paper_authors._id: 6789f776766dd160379b89fb
paper_authors.name: Zekun Xi
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/2fc34d2739b28c1089b20e7a7fa40f0e.svg
paper_authors.fullname: Xi Ze Kun
paper_authors.type: user
paper_authors.user: ZekunXi
paper_authors._id: 6789f776766dd160379b89fc
paper_authors.name: Wenbiao Yin
paper_authors._id: 6789f776766dd160379b89fd
paper_authors.name: Jizhan Fang
paper_authors.status: admin_assigned
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paper_authors.fullname: Jizhan Fang
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paper_authors.user: JizhanFang
paper_authors._id: 6789f776766dd160379b89fe
paper_authors.name: Jialong Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: Jialong Wu
paper_authors.type: user
paper_authors.user: callanwu
paper_authors._id: 6789f776766dd160379b89ff
paper_authors.name: Runnan Fang
paper_authors.status: admin_assigned
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paper_authors._id: 63d32cd7b734eaa4d4fa410b
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paper_authors.fullname: Runnan Fang
paper_authors.type: user
paper_authors.user: Runnaning
paper_authors._id: 6789f776766dd160379b8a00
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:25:07.769Z
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paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 6789f776766dd160379b8a01
paper_authors.name: Jiang Yong
paper_authors._id: 6789f776766dd160379b8a02
paper_authors.name: Pengjun Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:32:07.000Z
paper_authors._id: 63a091e42fabbbb89991f5ce
paper_authors.avatarUrl: /avatars/d55485b06461764c36c9edf9d6e8892c.svg
paper_authors.fullname: pengjun xie
paper_authors.type: user
paper_authors.user: xpjandy
paper_authors._id: 6789f776766dd160379b8a03
paper_authors.name: Fei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:32:22.107Z
paper_authors._id: 635b8b6a37c6a2c12e2cce00
paper_authors.avatarUrl: /avatars/229fb72180529141515d1df797b33709.svg
paper_authors.fullname: Fei Huang
paper_authors.type: user
paper_authors.user: hzhwcmhf
paper_authors._id: 6789f776766dd160379b8a04
paper_authors.name: Huajun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:32:36.118Z
paper_authors._id: 64931296137833d7ec7689cd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64931296137833d7ec7689cd/TBihNdp1ZwIWjhfAWjRr6.jpeg
paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: huajunsir
paper_title: OmniThink: Expanding Knowledge Boundaries in Machine Writing through
    Thinking
paper_summary: Machine writing with large language models often relies on
  retrieval-augmented generation. However, these approaches remain confined
  within the boundaries of the model's predefined scope, limiting the generation
  of content with rich information. Specifically, vanilla-retrieved information
  tends to lack depth, utility, and suffers from redundancy, which negatively
  impacts the quality of generated articles, leading to shallow, repetitive, and
  unoriginal outputs. To address these issues, we propose OmniThink, a machine
  writing framework that emulates the human-like process of iterative expansion
  and reflection. The core idea behind OmniThink is to simulate the cognitive
  behavior of learners as they progressively deepen their knowledge of the
  topics. Experimental results demonstrate that OmniThink improves the knowledge
  density of generated articles without compromising metrics such as coherence
  and depth. Human evaluations and expert feedback further highlight the
  potential of OmniThink to address real-world challenges in the generation of
  long-form articles.
paper_ai_summary: OmniThink is a machine writing framework that simulates human iterative learning to enhance the quality and depth of generated long-form articles.
paper_ai_keywords: retrieval-augmented generation predefined scope vanilla-retrieved information iterative expansion knowledge density coherence depth