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Equation 38 · Part 3 · AI Feeds on the Distance Between an Intention and an Outcome

Symbol alpha_m

logit⁡Pr⁡(Yimtr=1)=β0+αm+β1qtr+β2dt+β3log⁡(1+ht)+urepo(t)+ut.\operatorname{logit}\Pr(Y_{imtr}=1)= \beta_0+\alpha_m+\beta_1 q_{tr}+\beta_2 d_t+ \beta_3\log(1+h_t)+u_{\mathrm{repo}(t)}+u_t.
αm\alpha_m

What this part means

model or agent-configuration identity.

Its job in the formula

alphama_m is one of the signed contributions combined to compute the quantity on the left.

Where the article explains it

The terms urepo(t)u_{\mathrm{repo}(t)} and utu_t are repository and task random effects, while αm\alpha_m represents model or agent-configuration identity.

The passage around this formula

…static dependency reach, test-suite scope, and selected task-family label. It is deliberately conventional and admittedly imperfect. The terms urepo(t)u_{\mathrm{repo}(t)} and utu_t are repository and task random effects, while αm\alpha_m represents model or agent-configuration identity. The baseline includes model identity, token length, ordinary difficulty, and task horizon exactly so that the new variable cannot win by repeating any one of them.

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Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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Sources cited in the article section

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