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Equation 2 · What Claude's Capability Evaluations Actually Measure

What does this equation mean?

p(t)=11+exp⁡(a+blog⁡t),p(t) = \frac{1}{1 + \exp\left(a + b \log t\right)},

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Start with1
Divide by1 + exp(a + b log t)
This relates top(t)
How to read the two sides of this formula. Follow the article passage for the meaning of each quantity.

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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pp

Symbol p

p occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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tt

Symbol t

t occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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aa

Symbol a

a occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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bb

Symbol b

b occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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fraction

fraction

Divide the expression above the line by the one below it.

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addition

addition

Add the term after the plus sign to the term or group before it.

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11

Numerator: 1

The complete quantity above the fraction bar.

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1+exp⁡(a+blog⁡t)1 + \exp\left(a + b \log t\right)

Denominator: 1 + exp(a + b log t)

The complete quantity below the fraction bar; it must be nonzero for this division.

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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.

What the article says around this equation

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: p(t)=11+exp⁡(a+blog⁡t)p(t) = \frac{1}{1 + \exp\left(a + b \log t\right)}. with b > 0 , so predicted success falls as human-equivalent task length grows. The reported time horizon TxT_x 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…
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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: p(t)=11+exp⁡(a+blog⁡t)p(t) = \frac{1}{1 + \exp\left(a + b \log t\right)}. with b > 0 , so predicted success falls as human-equivalent task length grows. The reported time horizon TxT_x 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 headline number from the identical underlying data.

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