We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writing process, including citation recommendation, they have also led to the emergence of hallucinated citations that do not correspond to any existing work. Such citations not only undermine the credibility of scientific papers but also impose an additional burden on reviewers and authors, who must manually verify their validity during the review process. In this study, we formalize hallucinated citation detection as an NLP task and provide a corresponding toolkit as a practical foundation for addressing this problem. Our package is lightweight and can perform verification in seconds on a standard laptop. It can also be executed entirely offline and runs efficiently using only CPUs. We hope that HalluCiteChecker will help reduce reviewer workload and support organizers by enabling systematic pre-review and publication checks. Our code is released under the Apache 2.0 license on GitHub and is distributed as an installable package via PyPI. A demonstration video is available on YouTube.

翻译：我们提出HalluCiteChecker，一个用于检测和验证科学论文中幻觉引用的工具包。尽管AI辅助技术已经转变了学术写作流程（包括引用推荐），但它们也导致了与任何现有工作不对应的幻觉引用的出现。此类引用不仅削弱了科学论文的可信度，还给审稿人和作者带来了额外负担——他们必须在审稿过程中手动验证引用的真实性。在本研究中，我们将幻觉引用的检测形式化为一个自然语言处理任务，并提供一个相应的工具包作为解决该问题的实用基础。我们的软件包轻量高效，可在标准笔记本电脑上数秒内完成验证；同时支持完全离线运行，且仅使用CPU即可高效执行。我们希望HalluCiteChecker能通过实现系统化的预审和出版前检查，减轻审稿人工作负担并支持会议组织者。我们的代码已基于Apache 2.0许可证在GitHub上公开发布，同时可通过PyPI作为可安装包分发。配套演示视频已在YouTube上线。