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Equation 45 · Part 1 · AI Feeds on the Distance Between an Intention and an Outcome

Symbol Y_imtr

logit⁡Pr⁡(Yimtr=1)=baseline+γ1Gtr+γ2Gtrlog⁡(1+ht).\operatorname{logit}\Pr(Y_{imtr}=1)=\text{baseline}+ \gamma_1G_{tr}+\gamma_2G_{tr}\log(1+h_t).
YimtrY_{imtr}

What this part means

YiY_imtr appears in the conditional probability being evaluated. The vertical bar identifies the information or condition supplied to that probability.

Its job in the formula

YiY_imtr appears in the conditional probability being evaluated. The vertical bar identifies the information or condition supplied to that probability.

The passage around this formula

The challenger adds GtrG_{tr} , its uncertainty-aware estimate, and a preregistered interaction with task horizon: logit⁡Pr⁡(Yimtr=1)=baseline+γ1Gtr+γ2Gtrlog⁡(1+ht)\operatorname{logit}\Pr(Y_{imtr}=1)=\text{baseline}+ \gamma_1G_{tr}+\gamma_2G_{tr}\log(1+h_t). The primary scoreboard is not an in-sample coefficient. It is leave-one-repository-out predictive log loss, calibration slope, and Brier score, each computed before inspecting a graph of agents. The transfer scoreboard withholds whole task families or repositories, then asks which model better predicts the outcome from the descriptions and annotations already frozen. A positive in-sample γ1\gamma_1 is not a result; a redaction variable is almost designed to correlate with difficulty. Only lower held-out error and better calibration earn the claim that it carries…

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Sources cited in the article section

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