Published equation contexts

$\delta_{\mathrm{contam}} \geq 0$

Why this formula appears here

where $a_{\mathrm{true}}$ is the capability the benchmark intends to measure, $\delta_{\mathrm{contam}}$ $\geq$ 0 is a one-directional inflation term from training-set overlap, and $\varepsilon_{\mathrm{config}}$ is noise contributed by harness, prompt, effort level, and grading choices, which can push the observed score in either direction. Two systems’ $s_{\mathrm{obs}}$ values can differ substantially even when their $a_{\mathrm{true}}$ values are identical, and can appear equal even when their $a_{\mathrm{true}}$ values are not, purely through the other two terms. Nothing about this requires bad faith on anyone’s part; it follows from the measurement being taken at all.

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\delta_{\mathrm{contam}}

Symbol delta_contam

delt $a_c$ ontam is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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Published contexts (1)

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\delta_{\mathrm{contam}} \geq 0

Equation 9 · Model Evaluation

Frontier AI Model Comparisons: A First-Principles Introduction

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

where $a_{\mathrm{true}}$ is the capability the benchmark intends to measure, $\delta_{\mathrm{contam}}$ $\geq$ 0 is a one-directional inflation term from training-set overlap, and $\varepsilon_{\mathrm{config}}$ is noise contributed by harness, prompt, effort level, and grading choices, which can push the observed score in either direction. Two systems’ $s_{\mathrm{obs}}$ values can differ substantially even when their $a_{\mathrm{true}}$ values are identical, and can appear equal even when their $a_{\mathrm{true}}$ values are not, purely through the other two terms. Nothing about this requires bad faith on anyone’s part; it follows from the measurement being taken at all.

Equation guide → · Article →

δcontam≥0\delta_{\mathrm{contam}} \geq 0δcontam​≥0