用可操控的智能体系统辅助新手发现研究空白并生成靠谱假设。
AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research
- 用户可控制的智能体工作流，全程透明可追溯。
- 动态生成查询树与论文语义关系图，可视化探索路径。
- 适合科研新手快速上手陌生领域，提升研究信心。
文献研究的目标各异，从理解陌生主题到为新项目生成假设。对缺乏经验的研究者而言，发现现有文献缺口并提出可行假设尤为关键却困难。现有通用深度研究工具不针对此场景，效果不佳；而大语言模型的黑箱特性与幻觉问题更引发信任危机。本文提出人机协同可视化系统AwesomeLit，具备三大创新：透明可调控的智能体工作流、动态生成的查询探索树（可视化探索路径与来源）、语义相似性视图（呈现论文间关联）。系统帮助用户从模糊意图逐步聚焦至具体研究课题。一项面向多位早期研究者的定性研究显示，AwesomeLit能有效辅助探索陌生领域、识别有前景的研究方向，并增强对研究结果的信心。
原文摘要 · Abstract (English)
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.
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