用可验证的流程记录AI在科研中的每一步贡献，让研究过程透明可审计。
F(AI)2R: Who Did What, and Who Checked? Verifiable AI Provenance as an Executable Skill
- 构建AI全流程参与的研究溯源框架，支持自动化生成与人工审核
- 所有研究活动、声明和来源均记录在可追溯的证明图中
- 适合关注科研可信度与AI协作规范的研究者使用
F(AI)2R 是一种将AI纳入研究闭环的可验证方法：先由AI辅助撰写与重构研究产物，再通过机器可读的审计机制对每个成果进行验证。当前AI虽广泛参与论文起草、修改与验证，但其贡献往往未被以可审计的形式记录。本文在原有实验基础上，将溯源模型拓展为 aiprov——一个覆盖所有AI参与产物的 PROV-O 扩展，并将其封装为可执行技能：系统自动请求作者ORCID ID，从公共注册表中确认身份，搭建持续集成流程，仅当图结构符合规范时才允许提交，并发布本论文的最新构建版本。该论文自身即为案例，其生产过程中每项活动、声明与来源均记录于仓库的溯源图中，遵循两项不变原则：无孤立声明，且验证层级只能由人类授予。
原文摘要 · Abstract (English)
F(AI)2R is FAIR research with AI in the loop, twice: an AI-assisted authoring pass and a machine-readable audit pass over every artefact. AI systems now draft, refactor, and verify research artefacts, yet their contributions are rarely recorded in a form a later human or machine can audit. Building on the original F(AI)2R experiment, we generalize its provenance model beyond scholarly writing into aiprov, a PROV-O extension covering any AI-in-the-loop artefact, and we package the method as an executable skill that an AI agent operates itself: setup asks the human operator for their ORCID ID, resolves their identity from the public registry, and scaffolds continuous integration that gates every push on graph conformance and publishes the current build of this very paper. The paper is its own case study. Every activity, claim, and source in its production is recorded in the repository's provenance graph under two invariants: no parentless claim, and verification rungs that only humans may grant.
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