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Why this formula appears here
The derivative of with respect to q is steep near q = 1 , which is the good news — small per-step gains buy disproportionate horizon. The bad news is the same expression read the other way: sustaining an order of magnitude more steps requires driving per-step error down by an order of magnitude, and every one of the six problems above is a term in that per-step error which scale is not reducing. Verification, calibration, and memory are precisely the mechanisms that break the exponential by resetting accumulated uncertainty. Without them, capability gains are spent buying a slowly lengthening horizon rather than a qualitatively longer one.
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Equation 10 · Foundation Models
What We Still Cannot Do: Open Problems in Frontier Model Systems
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
The derivative of with respect to q is steep near q = 1 , which is the good news — small per-step gains buy disproportionate horizon. The bad news is the same expression read the other way: sustaining an order of magnitude more steps requires driving per-step error down by an order of magnitude, and every one of the six problems above is a term in that per-step error which scale is not reducing. Verification, calibration, and memory are precisely the mechanisms that break the exponential by resetting accumulated uncertainty. Without them, capability gains are spent buying a slowly lengthening horizon rather than a qualitatively longer one.