LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation

Problem
This paper addresses the gap in automatic scientific paper writing, specifically focusing on the Introduction generation task, which requires not only linguistic fluency but also logical coherence and verifiable accuracy. Existing AI-assisted methods primarily treat this task as a straightforward text generation problem, leading to issues such as hallucinated citations and lack of logical structure. The authors introduce the Content-Conditional Introduction Generation (CCIG) task, which emphasizes grounding the Introduction in the core evidence of the paper, a challenge that has not been adequately tackled in the literature. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose LECTOR, a Logic-Expression Co-Reinforcement Learning framework designed to enhance the quality of scientific Introduction generation. LECTOR operates in two main phases: first, it constructs a logic-reasoning graph from the main body of the paper, which serves as a verifiable logical blueprint. This graph captures the logical structure and relationships of the scientific arguments presented. Second, LECTOR employs a Logic-Expression Co-Rewarding mechanism that jointly optimizes for the structural fidelity of the graph and the quality of the generated narrative. The architecture leverages reinforcement learning to balance these objectives, ensuring that the generated Introduction adheres to the logical flow dictated by the graph while maintaining high-quality narrative standards.

Results
The authors evaluate LECTOR on a dataset derived from Nature Communications papers, demonstrating significant improvements over baseline models. Key metrics include a 26.7% increase in Graph Quality, an 8.6% improvement in Citation Quality, and a 3.3% enhancement in Paper Consistency. These results indicate that LECTOR not only generates more logically coherent Introductions but also improves the accuracy of citations, addressing critical shortcomings in existing methods.

Limitations
The authors acknowledge several limitations, including the reliance on a specific dataset, which may affect the generalizability of the results. Additionally, the complexity of the logic-reasoning graph construction may introduce challenges in scalability and applicability to diverse scientific domains. The paper does not address potential biases in citation selection or the implications of using a reinforcement learning framework, which may require extensive tuning and computational resources.

Why it matters
This work has significant implications for the field of automated scientific writing, particularly in enhancing the reliability and credibility of AI-generated content. By focusing on logical coherence and verifiable citations, LECTOR sets a new standard for Introduction generation, potentially reducing the prevalence of misinformation in scientific literature. The proposed framework could serve as a foundation for future research in AI-assisted writing tools, encouraging further exploration of structured reasoning in natural language generation tasks.

Authors: Jiabei Xiao, Yizhou Wang, Chen Tang, Pengze Li, Wanli Ouyang, Shixiang Tang
Source: arXiv:2605.25964
URL: https://arxiv.org/abs/2605.25964v1