Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules, benchmark revisions, and missingness. Repeated public archives for LiveBench and Open LLM Leaderboard v2 serve as the primary longitudinal record; LMArena provides a preference stress test; and GAIA and tau-bench contribute limited agentic pilots. Together, these archives instantiate a Bayesian inference problem: under a fixed reporting convention, one constructed terminal-only example over $1{,}000$ systems is compatible with two pre-terminal histories, yielding times of $23.03$ or $75.13$ to reach within $0.05$ of the ceiling under the same terminal-tail model. In synthetic posterior comparisons, action-facing diagnostics differ across observation regimes. The candidate selection-aware frontier model fails synthetic recovery, objective-archive prediction, preference transfer, and uncertainty calibration; correspondingly, fixed audit gates reject its stronger claims. An archive-and-adjudication protocol reconstructs public evaluation histories, isolates a verified timing boundary, and falsifies unsupported frontier claims.

翻译：公共AI评估常被解读为最终排行榜，然而其底层证据是由报告规则、基准修订和数据缺失塑造的选择性时间序列。LiveBench和Open LLM Leaderboard v2的重复公共档案组成了纵向基础记录；LMArena提供了偏好压力测试；GAIA与tau-bench则贡献了有限的智能体实验。这些档案共同构成了一个贝叶斯推断问题：在固定报告惯例下，一个仅含终端数据、涵盖$1{,}000$个系统的构造范例，与两种终端前历史数据相兼容，在相同终端尾端模型下分别需要23.03或75.13个时间单位才能达到距天花板0.05以内的效果。在合成后验比较中，面向行动的诊断结果因观测机制而异。基于候选者选择的前沿模型未能实现合成恢复、目标档案预测、偏好迁移及不确定性校准；相应地，固定审计关口否决了其较强的主张。一种档案与裁决协议能够重建公共评估历史、确定经验证的时间边界，并证伪无法支持的前沿主张。