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OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking

Paper · 2025 · Zekun Xi, Wenbiao Yin, Jizhan Fang et al.

Summary

LLM-based long-form writing usually depends on retrieval-augmented generation, but standard retrieval tends to return shallow, repetitive, and unoriginal material. Role-play systems such as STORM and Co-STORM gather more perspectives, yet still struggle to move past what the model already "knows." The authors describe two limits behind this: a Knowledge Information Boundary (too little useful knowledge is collected) and a Knowledge Cognition Boundary (the model organizes and uses what it collects poorly). Their framework, OmniThink, imitates a learner who slowly deepens their grasp of a topic. It grows a hierarchical information tree through repeated rounds of targeted search. After each round, it distills the new findings into a conceptual pool, which guides the next round of exploration. Only after this loop does it build an outline and write the article. On the WildSeek dataset, OmniThink produced articles with higher knowledge density while keeping coherence and depth intact. Human and expert evaluations supported these results. The method is model-agnostic, so it can be added to existing writing pipelines.

Key points

  • New metric: The paper introduces Knowledge Density (KD), which estimates how much of an article's content is useful information. It is reported alongside existing metrics on the WildSeek datasets.
  • Two core components:
    • The information tree grows by splitting leaf nodes into subtopics and running a separate retrieval for each one.
    • The conceptual pool stores the filtered insights drawn from each new layer of leaves.
  • Comparison with baselines:
    • STORM uses no dynamic retrieval, structured memory, or reflective thinking.
    • Co-STORM adds structured memory only.
    • OmniThink combines all three.
  • Ablation findings:
    • Expansion contributes more than reflection to knowledge density, breadth, and depth.
    • Reflection drives novelty, which suggests the cognition boundary is what restricts originality.
    • One variant tested Qwen2.5-7b-instruct as the reflection model.
  • Limits of deeper iteration:
    • Gains in knowledge density and information diversity slow sharply as expansion and reflection go deeper, pointing to other, not yet identified boundaries.
    • The authors also list text-only input (no multimodal data) and an academic-sounding style as limitations.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Alibaba GroupHangzhou · China168 works · 15,386 citations🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🏢Alibaba Group (Cayman Islands)George Town · Cayman Islands32 works · 3,389 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

Cite

Cite as Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen and Ningyu Zhang. 2025. OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.50

@inproceedings{xi2025omnithink,
  title = {{OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking}},
  author = {Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen and Ningyu Zhang},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.50},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.50}
}

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