Checked fact 42778 Nov 2026Research

The METR paper fits a logistic curve of success probability against the log of human task time (section 3.1); METR's page says it fits a logistic curve to predict the probability that a model completes tasks.

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The exact words it rests on

p_success(agent, task) = sigma((log h_agent - log t_task) * beta_agent) where t_task is the geometric mean time of successful human baselines ... with h_agent representing the 50% time horizon. / page: For each agent, we fit a logistic curve to predict the probability it successfully completes tasks as a function of human task duration.

What the source said when we opened it, on 8 Nov 2026.

The source

Measuring AI Ability to Complete Long Tasks (arXiv 2503.14499v3)
METR

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Checked

Checked by the notis newsroom on 8 Nov 2026, against the source above.

In the story

How METR's time horizon rates AI agents by human task length 8 Nov 2026

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“The METR paper fits a logistic curve of success probability against the log of human task time (section 3.1); METR's page says it fits a logistic curve to predict the probability that a model completes tasks.” ai notis, checked 8 Nov 2026. Source: Measuring AI Ability to Complete Long Tasks (arXiv 2503.14499v3) (METR). https://ainotis.com/c/4277

As a reference (APA style)

ai notis. (2026, November 8). The METR paper fits a logistic curve of success probability against the log of human task time (section 3.1); METR's page says it fits a logistic curve to predict the probability that a model completes tasks [Checked claim 4277]. ai notis. https://ainotis.com/c/4277

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