Точка Синхронізації

A team of researchers released a comprehensive study on the arXiv preprint server in February 2025 assessing the reproducibility of published work within the field of evolutionary computation. The investigation, titled "Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment," was conducted to address the lack of empirical evidence regarding how well algorithms and experimental protocols are documented in modern scientific literature. By utilizing both human evaluation and Large Language Models (LLMs), the authors sought to identify critical gaps in how artifacts are shared, which is essential for verifying the validity of computational experiments. The core of the research emphasizes that because evolutionary computation relies heavily on stochastic processes and complex experimental configurations, clear documentation is vital. However, the study points out that despite a growing awareness of the need for open science, many published papers still fall short of providing the necessary transparency. The researchers employed a novel methodology that compares traditional human peer-assessment against automated assessments generated by LLMs, potentially paving the way for more efficient systematic reviews of scientific integrity in the future. Beyond just identifying failures in documentation, the paper serves as a call to action for the technology and research community to standardize their reporting protocols. The findings suggest that the integration of AI-based assessment tools could become a standard part of the peer-review process to ensure that all necessary experimental parameters, source codes, and data artifacts are readily available. By highlighting these systemic issues, the study aims to foster a culture of transparency that protects the credibility of evolutionary computation as the field continues to evolve and integrate with broader artificial intelligence frameworks.