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Equation 14 · Part 4 · OpenAI and Claude on Agentic Coding: What the Independent Evidence Actually Shows

multiplication

sadj≈sreported×(1−fp)s_{\mathrm{adj}} \approx s_{\mathrm{reported}} \times (1-\mathrm{fp})
multiplication

What this part means

Multiply the quantities on either side.

Its job in the formula

Multiply the quantities on either side.

The passage around this formula

The size of that drop is close to what a simple deflation model predicts. If sreporteds_{\mathrm{reported}} is the score under the original oracle and fp\mathrm{fp} is the false-positive rate the adversarial suite exposes, then sadj≈sreported×(1−fp)s_{\mathrm{adj}} \approx s_{\mathrm{reported}} \times (1-\mathrm{fp}). gives 0.7880 ×\times (1 - 0.1971) ≈\approx 0.633 — within a percentage point of the measured 0.622 [ 14 ] . That closeness is a coincidence of rounding as much as a proof of the model, since rejected patches are not independent of task difficulty, but the approximation is useful precisely because it names the assumption plainly: a benchmark score is a joint statement about the system under test and the strength of the judge grading it, and when the judge gets…

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Multiplication scales one quantity by another. A dot, a cross, or adjacent symbols can indicate a product.

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Sources cited in the surrounding passage

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