Benchmarks increasingly guide deployment, procurement and scientific screening, yet a score supports only the response it records, not necessarily the deployment action. We introduce deployment-complete benchmarking, which tests whether benchmark evidence determines a deployment action. A benchmark is complete for a claim exactly when the action is constant on each evidence fiber; mixed fibers expose missing deployment information, and completion curves quantify the evidence required to resolve ambiguity. In controlled response spaces, benchmark-channel conformal coverage of 94.98% transferred poorly to an unmeasured deployment channel (10.07%), whereas response-rank intervals achieved 94.91% coverage; even zero benchmark error certified only 45.4% of candidates at the largest residual size. Public audits revealed incompleteness, including 97.9% mixed Tox21 fibers and zero median certifiable fraction in main Matbench and JARVIS audits. In held-out replays, certify-then-acquire reduced false decisions from 1.19% to 0.027% in Tox21 and from 20.3% to 0.128% in JARVIS, while changing model choice and identifying deployment-relevant probes. Deployment-ready benchmarks should report evidence, supported actions, ambiguity and completion cost rather than scores alone.

翻译：基准测试日益指导部署、采购和科学筛选，但一个评分仅支持它所记录的响应，不一定支持部署行为。我们提出部署完备的基准测试，用于检验基准证据是否能确定部署行为。一个基准测试对于某个主张是完备的，当且仅当该动作在每个证据纤维上是恒定的；混合纤维暴露出缺失的部署信息，完备性曲线则量化了解除歧义所需的证据。在受控响应空间中，基准通道的共形覆盖率为94.98%，但迁移到未测量的部署通道时表现不佳（仅为10.07%），而响应排名区间实现了94.91%的覆盖率；即使在最大残差规模下，零基准误差也仅能认证45.4%的候选对象。公开审计揭示了不完备性问题，包括97.9%的混合Tox21纤维以及Matbench和JARVIS主要审计中零中位数可认证比例。在留出重放测试中，先认证后获取策略将Tox21中的错误决策从1.19%降至0.027%，在JARVIS中从20.3%降至0.128%，同时改变了模型选择并识别出与部署相关的探针。面向部署的基准测试应报告证据、支持的动作、歧义性以及完备成本，而不仅仅是评分。