There are different goals for literature research, from understanding an unfamiliar topic to generate hypothesis for the next research project. The nature of literature research also varies according to user's familiarity level of the topic. For inexperienced researchers, identifying gaps in the existing literature and generating feasible hypothesis are crucial but challenging. While general ``deep research'' tools can be used, they are not designed for such use case, thus often not effective. In addition, the ``black box" nature and hallucination of Large Language Models (LLMs) often lead to distrust. In this paper, we introduce a human-agent collaborative visualization system AwesomeLit to address this need. It has several novel features: a transparent user-steerable agentic workflow; a dynamically generated query exploring tree, visualizing the exploration path and provenance; and a semantic similarity view, depicting the relationships between papers. It enables users to transition from general intentions to detailed research topics. Finally, a qualitative study involving several early researchers showed that AwesomeLit is effective in helping users explore unfamiliar topics, identify promising research directions, and improve confidence in research results.

翻译：文献研究具有不同目标，从理解陌生领域到为下一个研究项目提出假设。文献研究的性质也因用户对主题的熟悉程度而异。对于缺乏经验的研究人员，识别现有文献中的空白并提出可行假设至关重要但极具挑战性。尽管通用的"深度研究"工具可以用于此目的，但它们并未针对该应用场景设计，因此往往效果不佳。此外，大型语言模型(LLMs)的"黑箱"特性和幻觉问题常导致用户不信任。本文提出了一种人机协同的可视化系统AwesomeLit来满足这一需求。该系统具有多项创新特性：透明且用户可控的智能体工作流；动态生成的查询探索树，可可视化探索路径与来源；以及表征论文间关系的语义相似性视图。该系统使用户能够从笼统意图过渡到具体研究主题。最后，一项涉及多名初级研究人员的定性研究表明，AwesomeLit能有效帮助用户探索陌生主题、识别有前景的研究方向，并提升对研究成果的信心。