发表机构
Korea Advanced Institute of Science and Technology（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过固定计算证据测试编辑路由对AI辅助科学写作的影响，发现将比较分配到不同位置会改变数值限定的保留，提出针对性放置规则可改善限定表达。
AI 中文摘要
大型语言模型日益增多地分析计算结果并起草手稿，这使得可靠的沟通与正确的分析同等重要。利用固定的计算证据，我们测试了在研究工作流程的其他位置分配跨建模选择的比较是否改变手稿报告。在受限的句子写作任务中，Anthropic的Claude Sonnet 5在详细比较被分配到小组存储库时经常省略数值限定，但当相同的比较被分配到补充信息或其自己的工作笔记时，则更常保留这些限定；Claude Opus 5对此较不敏感。这些效应并未遵循简单的可访问性排序。一个有针对性的放置规则在很大程度上恢复了句子级别的限定，而一个通用的准确性提醒则没有。较长的贡献保留了数值限定，尽管跨计算设置的某些摘要仍被重定向到存储库。因此，在AI工作流程中记录上下文并不能确保其在读者遇到结果的地方得到传达。
英文摘要
Large language models increasingly analyze computational results and draft manuscripts, making reliable communication as important as correct analysis. Using fixed computational evidence, we tested whether assigning comparisons across modeling choices elsewhere in a research workflow changes manuscript reporting. In constrained sentence-writing tasks, Anthropic's Claude Sonnet 5 often omitted numerical qualifications when detailed comparisons were assigned to a group repository, but retained them more often when the same comparison was assigned to Supporting Information or its own working notes; Claude Opus 5 was less sensitive. These effects did not follow a simple accessibility ordering. A targeted placement rule largely restored sentence-level qualification, whereas a generic accuracy reminder did not. Longer contributions retained numerical qualifications, although some summaries across computational settings were still redirected to the repository. Thus, documenting context within an AI workflow does not ensure its communication where readers encounter the result.
