Abstract
Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While evaluating novelty of scientific papers is crucial for peer review, it requires extensive knowledge of related work, something not all reviewers have. While recent work on LLM-assisted scientific literature analysis supports literature comparison, existing approaches offer limited transparency and lack mechanisms for result traceability via an information retrieval module. To address this gap, we introduce $\textbf{GraphMind}$, an easy-to-use interactive web tool designed to assist users in evaluating the novelty of scientific papers or drafted ideas. Specially, $\textbf{GraphMind}$ enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights. $\textbf{GraphMind}$ enables users to annotate key elements of a paper, explore related papers through various relationships, and assess novelty with contextual insight. This tool integrates external APIs such as arXiv and Semantic Scholar with LLMs to support annotation, extraction, retrieval and classification of papers. This combination provides users with a rich, structured view of a scientific idea's core contributions and its connections to existing work. $\textbf{GraphMind}$ is available at this https URL and a demonstration video at this https URL. The source code is available at this https URL.
Abstract (translated)
大型语言模型（LLMs）展示了强大的推理和文本生成能力，因此被用于科学文献分析，包括新颖性评估。尽管在同行评审中评价科学论文的新颖性至关重要，但这项工作需要广泛了解相关领域的工作，而这并不是所有审稿人都具备的。 虽然最近关于利用LLM辅助进行科学文献分析的研究支持了文献比较的功能，但是现有方法提供的透明度有限，并且没有通过信息检索模块实现结果可追溯性的机制。为解决这一缺口，我们引入了一种易于使用的交互式网络工具$\textbf{GraphMind}$，旨在帮助用户评估科学论文或草稿想法的新颖性。 具体来说，$\textbf{GraphMind}$使用户能够捕捉一篇科学论文的主要结构，从不同角度探索相关概念，并通过提供可验证的上下文洞察来评估新颖性。该工具允许用户标注论文的关键元素、通过各种关系探索相关的其他文献，并利用情境洞察进行新颖性的评估。 这种结合了arXiv和Semantic Scholar等外部API与LLM的技术为用户提供了一个丰富且结构化的视角，展示科学想法的核心贡献及其与现有工作的联系。 $\textbf{GraphMind}$可在[这个网址](https://example.com/graphmind)获取，并提供[演示视频链接](https://example.com/video)，源代码可访问[这里](https://example.com/sourcecode)。
URL
https://arxiv.org/abs/2510.15706
