GraphMind用交互式工具帮科研人员快速判断论文创新性。
GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery
- 结合LLM与外部数据库，自动提取论文核心要素
- 支持多维度关联检索，直观展示研究想法的背景关系
- 提供可验证的上下文依据，适合审稿人和科研新手使用
大型语言模型（LLMs）在科学文献分析中展现出强大的推理与文本生成能力，尤其适用于新颖性评估。然而，科学论文的新颖性判断需广泛了解相关工作，这对部分评审人而言存在知识盲区。现有基于LLM的方法虽能支持文献对比，但透明度低且缺乏结果可追溯机制。为此，我们提出GraphMind——一个易用的交互式网页工具，用于辅助用户评估科学论文或构思的新颖性。GraphMind允许用户捕获论文主干结构，通过多种关系探索相关研究，并基于可验证的上下文信息进行新颖性判断。该工具整合arXiv、Semantic Scholar等外部API与LLM，实现论文的标注、提取、检索与分类。这一组合为科学思想的核心贡献及其与已有工作的联系提供了丰富、结构化的视图。GraphMind已上线：https://oyarsa.github.io/graphmind，演示视频：https://youtu.be/wKbjQpSvwJg，源码见：https://github.com/oyarsa/graphmind。
原文摘要 · Abstract (English)
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.
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