Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery. Recent Large Language Model (LLM)-based methods often ground idea generation with retrieved literature, but the retrieved evidence is usually provided as flat text, such as titles, abstracts, or summaries. Such flat contexts may contain redundant or weakly relevant information, while making cross-paper relations among problems, methods, mechanisms, and findings difficult to identify and trace. To address this challenge, we propose Graph2Idea, a knowledge graph-guided framework for retrieval-augmented scientific idea generation.Graph2Idea first retrieves papers accordin...
| Authors | Xu Li, Hanzhe Tu, Xun Han |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 10 June 2026 |
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