用于文档内自适应AI文本筛查的两种共形构造
Two Conformal Constructions for Adaptive Within-Document AI-Text Screening
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中文总结 AI 辅助
针对AI文本筛查中的误报控制问题，提出两种基于共形推断的有限样本构造方法，分别通过联合界和完整路径最大值实现自适应停止，并证明边际控制与所需校准计数。
中文摘要 AI 辅助
我们研究在筛查人工智能（AI）生成的文本时对误报的控制。筛查程序根据观测证据选择文档前缀和检测器，并可能在耗尽检查预算之前停止。我们在文档层面可交换性的假设下，给出了两种有限样本构造，该假设适用于人类校准文档和新的零假设文档，且对文档内标记之间的依赖性没有任何限制。构造A注册一个有限的前缀-检测器得分族，并将其误报预算分配至它们的共形秩。一个联合界保护该族中任何已执行的子集。构造B校准一个开发阶段固定的自适应策略的完整路径最大值。每个部分路径最大值都受完整最大值的约束，因此一个终端共形秩可以在不分割误差预算的情况下保护提前停止。我们证明了在允许的检查路径上对任何误报的边际控制，并推导了拒绝所需的校准计数。我们还陈述了带有额外假设的预言机测试、分布偏移和独立审计界。两种构造都在其指定范围内保护停止；两种证明均未构造e过程或证明共形秩相乘的合理性。检测能力和计算节省仍有待实证评估。
英文摘要
We study false-alert control when screening for text generated by artificial intelligence (AI). The screening procedure selects document prefixes and detectors from observed evidence and may stop before exhausting its inspection budget. We give two finite-sample constructions under document-level exchangeability between human calibration documents and a new null document, with no restriction on dependence among tokens within a document. Construction A registers a finite family of prefix-detector scores and allocates a false-alert budget across their conformal ranks. A union bound protects any executed subset of that family. Construction B calibrates the complete-path maximum of a development-fixed adaptive policy. Each partial-path maximum is bounded by the complete maximum, so a terminal conformal rank protects early stopping without splitting the error budget. We prove marginal control of any false alert across the permitted inspection path and derive necessary calibration counts for rejection. We also state oracle testing, distribution-shift, and independent-audit bounds with their additional assumptions. Both constructions protect stopping within their specified scope; neither proof constructs an e-process or justifies multiplying conformal ranks. Detection power and computational savings remain questions for empirical evaluation.
发表机构
- Bulacan State University（布拉坎州立大学）
机构由 AI 辅助整理，请以论文原文为准。
