![[2602.22215] Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22215] Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
Summary
This paper introduces GYWI, a system that enhances scientific idea generation by integrating co-author knowledge graphs with retrieval-augmented generation techniques, improving the contextual relevance and traceability of generated ideas.
Why It Matters
The integration of co-author graphs with LLMs addresses the challenge of generating scientifically relevant ideas with clear inspiration pathways. This advancement could significantly enhance research productivity and innovation in scientific fields by providing more reliable and contextually rich outputs.
Key Takeaways
- GYWI combines author knowledge graphs with retrieval-augmented generation for improved scientific idea generation.
- The system enhances the controllability and traceability of ideas generated by large language models.
- A hybrid retrieval mechanism is developed to optimize the depth and breadth of knowledge accessed.
- The proposed approach outperforms existing LLMs in novelty, reliability, and relevance of generated ideas.
- Comprehensive evaluation methods ensure robust assessment of the generated ideas across multiple dimensions.
Computer Science > Artificial Intelligence arXiv:2602.22215 (cs) [Submitted on 5 Dec 2025] Title:Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation Authors:Pengzhen Xie, Huizhi Liang View a PDF of the paper titled Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation, by Pengzhen Xie and Huizhi Liang View PDF HTML (experimental) Abstract:Large Language Models (LLMs) demonstrate potential in the field of scientific idea generation. However, the generated results often lack controllable academic context and traceable inspiration pathways. To bridge this gap, this paper proposes a scientific idea generation system called GYWI, which combines author knowledge graphs with retrieval-augmented generation (RAG) to form an external knowledge base to provide controllable context and trace of inspiration path for LLMs to generate new scientific ideas. We first propose an author-centered knowledge graph construction method and inspiration source sampling algorithms to construct external knowledge base. Then, we propose a hybrid retrieval mechanism that is composed of both RAG and GraphRAG to retrieve content with both depth and breadth knowledge. It forms a hybrid context. Thirdly, we propose a Prompt optimization strategy incorporating reinforcement learning principles to automaticall...
