🤖 AI Summary
Traditional benchmarks provide only aggregate scores, offering insufficient evidence to support reliable deployment decisions and thereby creating a disconnect between evaluation and action. To address this gap, this work proposes a “deployment-completeness” benchmarking framework, introducing novel metrics—evidence fibers, completeness curves, and certifiable proportions—alongside a systematic audit methodology comprising evidence fiber analysis, response ranking intervals, conformal coverage evaluation, and a certify-then-acquire decision pipeline. Empirical evaluation on benchmarks such as Tox21, Matbench, and JARVIS reveals that conventional approaches suffer a drastic drop in channel coverage to 10.07% under real-world deployment conditions. In contrast, the proposed method reduces error-driven deployment decisions to 0.027% on Tox21 and 0.128% on JARVIS, substantially enhancing deployment reliability.
📝 Abstract
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.