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 https://oyarsa.github.io/graphmind and a demonstration video at https://youtu.be/wKbjQpSvwJg. The source code is available at https://github.com/oyarsa/graphmind.

翻译：大型语言模型（LLM）展现出强大的推理与文本生成能力，这促使人们将其应用于科学文献分析，包括新颖性评估。尽管评估科学论文的新颖性对同行评审至关重要，但这需要广泛的相关工作知识，而并非所有评审者都具备。虽然近期关于LLM辅助科学文献分析的研究支持文献比较，但现有方法透明度有限，且缺乏通过信息检索模块实现结果可追溯性的机制。为弥补这一不足，我们引入了$\textbf{GraphMind}$，这是一个易于使用的交互式网络工具，旨在帮助用户评估科学论文或草拟想法的新颖性。具体而言，$\textbf{GraphMind}$使用户能够捕捉科学论文的主要结构，通过多种视角探索相关想法，并通过提供可验证的上下文洞察来评估新颖性。该工具集成了arXiv和Semantic Scholar等外部API与LLM，以支持论文的标注、提取、检索和分类。这种结合为用户提供了关于科学思想核心贡献及其与现有工作联系的丰富结构化视图。$\textbf{GraphMind}$可通过https://oyarsa.github.io/graphmind 访问，演示视频位于https://youtu.be/wKbjQpSvwJg。源代码发布于https://github.com/oyarsa/graphmind。