Developers increasingly provide AI coding assistants with persistent context through configuration files such as CLAUDE.md, AGENTS.md, and .cursorrules. These files describe code elements, architecture, and development conventions, forming the context that guides AI tool behavior across sessions. As software evolves, this context can become stale, a phenomenon we call context rot. While AI configuration artifacts are new, the underlying consistency problem connects to decades of software documentation research. Researchers have built tools to check consistency between documentation and code, spanning README files, code comments, API documentation, architecture descriptions, and installation instructions. We argue that this existing toolbox is an immediate starting point for detecting context rot, and we present a research roadmap mapping documentation consistency approaches to corresponding problems in this new setting. As preliminary evidence, applying an existing README/wiki consistency checker to a statistically representative sample of 356 repositories identifies stale code element references in 23.0% of repositories, showing that traditional documentation consistency tools can already surface context rot.

翻译：开发者越来越多地通过配置文件（如CLAUDE.md、AGENTS.md和.cursorrules）为AI编码助手提供持久性上下文。这些文件描述代码元素、架构和开发规范，形成跨会话指导AI工具行为的上下文。随着软件演进，此上下文可能变得陈旧——我们将此现象称为"上下文衰减"。尽管AI配置工件是新兴事物，但其背后的一致性难题与数十年的软件文档研究一脉相承。研究人员已构建了检查文档与代码一致性的工具，涵盖README文件、代码注释、API文档、架构描述和安装说明等场景。我们认为现有工具箱可作为检测上下文衰减的直接起点，并提出将文档一致性方法映射至这一新设情境下同类问题的研究路线图。初步验证表明：对356个具有统计代表性的代码库应用现有README/维基一致性检查工具后，23.0%的代码库中存在陈旧的代码元素引用——这证明传统文档一致性工具已可揭露上下文衰减现象。