---
格式版本: 2
标题: "Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution"
原文链接: "https://arxiv.org/abs/2608.10740"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10740"
---

## Computer Science > Artificial Intelligence

## Title:Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution

Authors:[Xun Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+X), [Yiying Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+Y), [Pengtao Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+P), [Xiao Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+X), [Suyu Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+S), [Xiaoyang Ye](https://arxiv.org/search/cs?searchtype=author&query=Ye,+X), [Ziyu Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+Z), [Yuan Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+Y), [Yangning Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Y), [Yinghui Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Y), [Wenhao Jiang](https://arxiv.org/search/cs?searchtype=author&query=Jiang,+W)

[View PDF](https://arxiv.org/pdf/2608.10740) [HTML (experimental)](https://arxiv.org/html/2608.10740v1)

> Abstract:Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature. Existing methods either treat papers as unstructured context or model scholarly evolution as isolated citation chains, overlooking interactions among research trajectories. We propose Tree-of-Ideas (ToI), a two-stage framework. EvoTrace reconstructs branching scholarly trajectories from citations, tracking evolving methods, resolved problems, and gaps. EvoAgent then reasons across trajectories to identify convergent problems and complementary solutions, generating grounded research ideas. Across six AI research topics, ToI achieves the highest score among automatic methods (6.27 vs. 5.36 for the strongest baseline on a 10-point scale), with strong Novelty (6.36) and Groundedness (7.00). Also, its score approaches that of human-paper references (6.29), demonstrating the value of cross-path evolutionary reasoning.

| Subjects: | Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.10740](https://arxiv.org/abs/2608.10740) \[cs.AI\] |
|  | (or [arXiv:2608.10740v1](https://arxiv.org/abs/2608.10740v1) \[cs.AI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10740](https://doi.org/10.48550/arXiv.2608.10740) |

## Submission history

From: Yiying Yang \[[view email](https://arxiv.org/show-email/7a263593/2608.10740)\]  
**\[v1\]** Tue, 11 Aug 2026 09:56:55 UTC (1,524 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10740) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
