Ensuring that code accurately reflects the algorithms and methods described in research papers is critical for maintaining credibility and fostering trust in AI research. This paper presents a novel system designed to verify code implementations against the algorithms and methodologies outlined in corresponding research papers. Our system employs Retrieval-Augmented Generation to extract relevant details from both the research papers and code bases, followed by a structured comparison using Large Language Models. This approach improves the accuracy and comprehensiveness of code implementation verification while contributing to the transparency, explainability, and reproducibility of AI research. By automating the verification process, our system reduces manual effort, enhances research credibility, and ultimately advances the state of the art in code verification.

翻译：确保代码准确反映研究论文中描述的算法与方法，对于维护人工智能研究的可信度与培养信任至关重要。本文提出了一种新颖的系统，旨在根据相应研究论文中概述的算法与方法验证代码实现。我们的系统采用检索增强生成技术从研究论文与代码库中提取相关细节，随后利用大型语言模型进行结构化比较。该方法提高了代码实现验证的准确性与全面性，同时有助于提升人工智能研究的透明度、可解释性与可复现性。通过自动化验证流程，本系统减少了人工工作量，增强了研究可信度，并最终推动了代码验证领域的技术发展。