Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems

The article argues that for frontier language models, precision—how consistently outputs cluster around the target—should be the key metric rather than capability, which measures average performance. It proposes a simple, non‑circular method to quantify precision by repeatedly scoring deterministic tasks and computing outcome consistency, and demonstrates how this metric can guide decisions about model improvements. The study shows that precision can reveal whether failures are due to systemic misalignment or random noise, and that real‑world measurement is more valuable than rule‑based benchmarks.

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• benchmarks