Abstract: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-Reinforcement Learning framework that can strictly follow the scientist's logic, add high-quality citations and keep structured expressions. LECTOR first constructs a logic-reasoning graph from the paper's main body to serve as a verifiable logical blueprint. Subsequently, it employs a Logic-Expression Co-Rewarding mechanism to jointly optimize for both the graph's structural fidelity and the final narrative's quality. We conduct a dataset from Nature Communications papers to assess our method. Extensive experiments show consistent improvements in both logic fidelity and Introduction generation quality metrics, e.g., Graph Quality (+26.7%), Citation Quality (+8.6%), and Paper Consistency (+3.3%). Code and data are available at this https URL.
| Comments: | 25 pages |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.25964 [cs.AI] |
| (or arXiv:2605.25964v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25964 arXiv-issued DOI via DataCite (pending registration) |
From: Jiabei Xiao [view email]
[v1]
Mon, 25 May 2026 15:41:16 UTC (1,097 KB)
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