论文是否讲完了全部故事？一个用于揭示生物信息学中隐藏实现差距的基准和框架
Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics
AI总结：
本文提出论文-代码一致性检测任务，构建首个生物信息学基准数据集BioCon，通过统一框架提升代码与论文的一致性与语义对齐。
摘要：
确保研究论文与其对应软件代码实现之间的一致性是保证科学发现可重复性和软件系统可靠性的基本前提。然而，这一问题目前关注有限，尤其是在生物信息学领域，论文中的方法描述与实际代码实现之间存在普遍不一致。为此，我们引入了一个新的研究任务，即论文-代码一致性检测，旨在表征论文中的方法描述与对应代码实现之间的跨模态语义对齐。在数据层面，我们构建了首个生物信息学领域的基准数据集BioCon，包含48个生物信息学软件项目及其相关论文。BioCon通过论文中句子级别的方法描述与代码片段的函数级别细粒度对齐，结合专家标注和难负样本采样策略，生成高质量的句子-代码配对数据集。在方法层面，我们提出了一种统一的跨模态一致性检测框架，利用预训练模型联合编码论文句子和代码函数。我们从三个角度进行了系统分析：句子级别分类、跨模态检索和项目级别一致性评估。实验结果表明，所提方法在一致性和语义对齐方面表现优异。整体而言，本文建立了首个系统化的论文-代码一致性分析基准和框架，开辟了新的研究方向，并为提高生物信息学软件的可重复性和可靠性提供了基础。
英文摘要：
As bioinformatics software is increasingly applied across a broader range of scenarios and the rapid development of large language models (LLMs) further lowers the barriers to software use and development, the composition of the bioinformatics research community is undergoing substantial change. Consequently, a growing number of researchers require a deeper understanding of methodological details and software behavior. In this context, systematically analyzing the relationship between paper descriptions and code implementations is emerging as an important new challenge in the field. To address this challenge, we introduce paper-code consistency analysis as a new research perspective and construct BioCon, the first benchmark dataset for paper-code consistency analysis in bioinformatics. Furthermore, we develop a unified cross-modal analysis framework to systematically investigate this problem from three perspectives: sentence-level detection, cross-modal retrieval, and project-level assessment. Experimental results demonstrate that the proposed framework can effectively model the semantic relationships between scientific publications and software implementations. Further case studies reveal that paper-code inconsistency is not a single phenomenon but arises from multiple underlying causes, among which Author-Perceived Non-Essential Details represents the most prevalent category. These findings suggest that paper-code consistency analysis is not merely a technical problem but also raises broader discussions regarding knowledge dissemination, the boundaries of code disclosure, and community norms. We hope that this work will encourage the bioinformatics community to re-examine the relationship between scientific publications and software implementations while providing a foundation for future research in paper-code consistency analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
