AI 中文总结
针对AI辅助研究写作缺乏可追溯责任历史的问题，该研究提出一套预注册过程观测的可审计性工程规范，通过git密封、红线门等技术实现审计，经实验验证其停止规则可有效终止不合规工作，相关工具包可供第三方复现指标。
AI 中文摘要
语言模型目前在研究产出中进行草稿撰写、分类和批评，但它们协助生成的成果几乎没有可追溯的责任历史。我们并非事后检测机器参与，而是在产出时构建了一套可审计性工程规范：使用带有锚定谱系的git密封、哈希绑定的来源、拒绝不合规成果并记录每次拒绝的红线门、跨模型角色分离，以及从预注册源进行程序化组装。合规性由指标卡进行检测，每张指标卡都带有预注册的盲点和证据地位，在其观测的预期案例之前冻结。在该案例中，被观测项目的预注册验证性测试在密封下执行并返回“不通过”，该项目的冻结停止规则违背其自身操作人员的意愿停止了工作。较低级别的回顾性案例涵盖了早于该协议的机制家族。当前观测结果是临时的；我们发布了一个包，第三方可通过该包重新计算所有主要指标。
英文摘要
Language models now draft, classify and criticise inside research production, yet the artifacts they help produce carry little accountable history. Rather than detecting machine involvement afterwards, we specify an auditability discipline built at production time: git sealing with an anchor lineage, hash-bound provenance, red-line gates that refuse non-compliant artifacts and log every refusal, cross-model role separation, and programmatic assembly from registered sources. Adherence is instrumented by metric cards, each carrying a pre-registered blind spot and evidential standing, frozen before the prospective case it observes. In that case the observed project's pre-registered confirmatory test was executed under seal and returned No-Go, and that project's frozen stopping rule halted the work, against its own operators. A lower-graded retrospective case covers families whose machinery predates the protocol. Current observations are provisional; we release a package from which a third party can recompute every primary metric.
Comments25 pages, 2 figures
