Equation 10 · Frontier AI Model Comparisons: A First-Principles Introduction
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
noise contributed by harness, prompt, effort level, and grading choices, which can push the observed score in either direction. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol varepsilon_config
noise contributed by harness, prompt, effort level, and grading choices, which can push the observed score in either direction.
subscript
The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.
How to interpret it
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What the article says around this equation
where is the capability the benchmark intends to measure, 0 is a one-directional inflation term from training-set overlap, and is noise contributed by harness, prompt, effort level, and grading choices, which can push the observed score in either direction. Two systems’ values can differ substantially even when their values are identical, and can appear equal even when their 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.
Sources cited in the article section
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