Symbol p
p occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
Read this term in its guide →Published equation contexts
Why this formula appears here
The method is a curve fit, not a lookup table, and stating it plainly exposes the assumption it rests on. For a task of human-expert duration t , METR fits a logistic curve to the model’s observed success rate across many tasks of varying length: . with b > 0 , so predicted success falls as human-equivalent task length grows. The reported time horizon at success level x is then the duration at which the fitted curve crosses that threshold, i.e. the t solving p(t) = x . Two things follow directly from this formulation that a single reported number obscures: the curve, not the crossing point, is the actual result, and a different chosen threshold x produces a different…
Read the representative guide
Symbol t
t occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
Read this term in its guide →Symbol a
a occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
Read this term in its guide →Symbol b
b occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
Read this term in its guide →Denominator: 1 + exp(a + b log t)
The complete quantity below the fraction bar; it must be nonzero for this division.
Read this term in its guide →How to interpret it
With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.
Research cited beside this formula
Published contexts (1)
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 2 · Foundation Models
What Claude's Capability Evaluations Actually Measure
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
The method is a curve fit, not a lookup table, and stating it plainly exposes the assumption it rests on. For a task of human-expert duration t , METR fits a logistic curve to the model’s observed success rate across many tasks of varying length: . with b > 0 , so predicted success falls as human-equivalent task length grows. The reported time horizon at success level x is then the duration at which the fitted curve crosses that threshold, i.e. the t solving p(t) = x . Two things follow directly from this formulation that a single reported number obscures: the curve, not the crossing point, is the actual result, and a different chosen threshold x produces a different…
Equation guide → · Article →