论决策值映射与表征依赖性 / On Decision-Valued Maps and Representational Dependence
1️⃣ 一句话总结
这篇论文提出了一种名为‘决策值映射’的框架,用于追踪和分析当同一数据采用不同表示形式时,计算引擎如何产生不同的离散决策结果,并开发了名为DecisionDB的基础设施来记录、复现和审计这些关系,从而将表征空间划分为稳定区域和边界,并将决策复用转化为一个可机械验证的条件。
A computational engine applied to different representations of the same data can produce different discrete outcomes, with some representations preserving the result and others changing it entirely. A decision-valued map records which representations preserve the outcome and which change it, associating each member of a declared representation family with the discrete result it produces. This paper formalizes decision-valued maps and describes DecisionDB, an infrastructure that logs, replays and audits these relationships using identifiers computed from content and artifacts stored in write-once form. Deterministic replay recovers each recorded decision identifier exactly from stored artifacts, with all three identifying fields matching their persisted values. The contribution partitions representation space into persistence regions and boundaries, and treats decision reuse as a mechanically checkable condition.
论决策值映射与表征依赖性 / On Decision-Valued Maps and Representational Dependence
这篇论文提出了一种名为‘决策值映射’的框架,用于追踪和分析当同一数据采用不同表示形式时,计算引擎如何产生不同的离散决策结果,并开发了名为DecisionDB的基础设施来记录、复现和审计这些关系,从而将表征空间划分为稳定区域和边界,并将决策复用转化为一个可机械验证的条件。
源自 arXiv: 2602.11295