LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation
AI Scientists have shown promising progress across multiple stages of the research pipeline, among which automatic scientific paper writing remains a formidable challenge. The Introduction writing is especially challenging, which demands not only linguistic fluency, but logical soundness and verifiable faithfulness. Most AI-assisted methods treat the task as text generation instead of reasoning and structuring, leading to severe drawbacks, e.g., hallucinating citations. To address this, we first formulate the Content-Conditional Introduction Generation (CCIG) task, which requires grounding the Introduction in the paper's core evidence. We then propose LECTOR, a novel Logic-Expression Co-R...
| Authors | Jiabei Xiao, Yizhou Wang, Chen Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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