AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research
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...
| Authors | Zefei Xie, Yuhan Guo, Kai Xu |
|---|---|
| Primary category | cs.HC |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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