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arXiv 提交日期: 2026-05-25
📄 Abstract - Deployment-complete benchmarking

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.

顶级标签: machine learning model evaluation benchmark
详细标签: benchmark completeness deployment transfer conformal prediction evidence fiber completion curve 或 搜索:

部署完备的基准测试 / Deployment-complete benchmarking


1️⃣ 一句话总结

本文提出“部署完备基准测试”的概念,旨在解决现有基准测试分数与实际部署决策脱节的问题,通过衡量证据是否足以支撑具体行动,并在多个公开数据集中验证了当前基准测试存在大量信息缺失,从而提出应报告证据、行动、歧义性和完成成本等更全面的评价指标。

源自 arXiv: 2605.25997