With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields. This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities. Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths. Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties. To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles. Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review.

本研究解决了科学发现日益分散的问题，尤其是在不同研究领域之间的比较与评估。我们提出了一种名为辩论树的框架，将科学论文转换为参与辩论的语言模型角色，以进行各自新颖性的探讨。实验结果表明，辩论树能够生成翔实的论证，有效对比论文，并支持研究人员进行文献综述。

辩论树：多角色辩论树激发科学比较分析的批判性思维