发表机构
Australian Institute for Machine Learning; Adelaide University（澳大利亚机器学习研究所; 阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出自适应保证框架，证明过去可测量的选择可保留鞅动力学但需付费启动修正，并确立切换成本界限，支持统计保证在变化下的重用与审计。
AI 中文摘要
自适应系统可以在改变其估计用途所保证的对象的同时，保留早期的估计。该保证取决于产生证据的实验，包括其目标是如何被选择的，以及哪些假设将其与当前声明联系起来。我们表明，过去可测量的选择保留了未来的鞅动力学，而不保留单位期望起始值。一个精确的有限反例，使用严格正族，以概率一反驳了已发表的、按所述陈述的变先验扩展，并排除了仅基于切换次数和误差水平的无限制界限。我们给出了一个付费启动的多切换修正，并确立了$m$作为$m$个假设的无结构族的锐利最坏情况期望起始时刻成本。然后，我们开发了自适应保证（Adaptive Assurance），这是一个框架，其中统计事件保留其实验、资金和结果绑定，而声明和前提依赖关系发生变化。兼容结果投影支持重用、选择性失效、新获取和可审计决策。两个历史可以具有相同的当前区域，却需要对后续编辑做出不同响应，从而使证据来源成为保证状态的一部分。在六个公开记录的领域中的研究，检验了异构结果和访问合同。组件干预暴露了错误的继承和不必要的重置。该框架将条件保证恢复与能力适应分开，并明确列出了证据、计算和验证的成本。
英文摘要
An adaptive system can retain an earlier estimate while changing what that estimate is used to guarantee. The guarantee depends on the experiment that produced the evidence, including how its target was selected and which assumptions connect it to the current claim. We show that past-measurable selection preserves future martingale dynamics without preserving a unit expected starting value. An exact finite counterexample, using a strictly positive family, refutes a published changing-prior extension as stated with probability one and rules out unrestricted bounds based only on switch count and the error level. We give a paid-start multi-switch correction and establish $m$ as the sharp worst-case expected starting-moment cost for an unstructured family of $m$ hypotheses. We then develop Adaptive Assurance, a framework in which statistical events retain their experiment, funding and outcome bindings while claims and premise dependencies change. Compatible-outcome projection supports reuse, selective invalidation, fresh acquisition and auditable decisions. Two histories can have identical current regions yet require different responses to a later edit, making evidence origin part of the assurance state. Studies in six public-recorded domains exercise heterogeneous outcomes and access contracts. Component interventions expose erroneous inheritance and unnecessary resets. The framework separates conditional guarantee recovery from competence adaptation, with explicit costs for evidence, computation and verification.
Comments28 pages, 4 figures, 9 tables; includes appendices
