标题： GraphMind：加速科学发现的交互式新颖性评估系统 显示英文标题

标题： GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery

摘要： 大型语言模型（LLMs）表现出强大的推理和文本生成能力，促使它们被用于科学文献分析，包括新颖性评估。 虽然评估科学论文的新颖性对于同行评审至关重要，但需要对相关工作有广泛的知识，而并非所有评审人员都具备这一点。 尽管近期关于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，并且缺乏通过信息检索模块进行结果可追溯性的机制。 为解决这一差距，我们引入了$\textbf{GraphMind}$，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。 特别地，$\textbf{GraphMind}$使用户能够捕捉科学论文的主要结构，通过各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。 $\textbf{GraphMind}$使用户能够标注论文的关键元素，通过各种关系探索相关论文，并通过上下文洞察评估新颖性。 该工具集成了arXiv和Semantic Scholar等外部API与LLMs，以支持论文的注释、提取、检索和分类。 这种组合为用户提供了科学理念核心贡献及其与现有工作的联系的丰富、结构化的视图。 $\textbf{GraphMind}$可在 https://oyarsa.github.io/graphmind 获取，演示视频可在 https://youtu.be/wKbjQpSvwJg 查看。 源代码可在 https://github.com/oyarsa/graphmind 获取。 显示英文摘要

摘要： 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.