Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of academic integrity and intellectual property, and (3) protection of information privacy. In this work, we present CiteLLM, a specialized agentic platform designed to enable trustworthy reference discovery for grounding author-drafted claims and statements. The system introduces a novel interaction paradigm by embedding LLM utilities directly within the LaTeX editor environment, ensuring a seamless user experience and no data transmission outside the local system. To guarantee hallucination-free references, we employ dynamic discipline-aware routing to retrieve candidates exclusively from trusted web-based academic repositories, while leveraging LLMs solely for generating context-aware search queries, ranking candidates by relevance, and validating and explaining support through paragraph-level semantic matching and an integrated chatbot. Evaluation results demonstrate the superior performance of the proposed system in returning valid and highly usable references.

翻译：大型语言模型（LLM）为提升学术活动效率创造了新的机遇；然而，人工智能辅助的伦理部署仍面临挑战，包括：（1）AI生成内容的可信度，（2）学术诚信与知识产权的维护，以及（3）信息隐私的保护。在本工作中，我们提出了CiteLLM，这是一个专为支持作者起草的主张与陈述提供可信参考文献发现而设计的智能代理平台。该系统引入了一种新颖的交互范式，将LLM功能直接嵌入LaTeX编辑器环境中，确保无缝的用户体验且无数据传出本地系统。为保证生成无幻觉的参考文献，我们采用动态学科感知路由，专门从可信的基于网络的学术存储库中检索候选文献，同时仅利用LLM生成上下文感知的搜索查询、按相关性对候选文献进行排序，并通过段落级语义匹配与集成的聊天机器人来验证和解释支持依据。评估结果表明，所提系统在返回有效且高度可用的参考文献方面表现出优越性能。