What does this equation mean?

It should not be read alone. The companion regressions for the middle and recency effects, reported directly beside it in the same appendix table set, show a different and more telling pattern. The middle-effect regression reports a Model T5 coefficient of -6.2258 carrying a standard error of 3.19 $\times$ 10^{4} , and a Model Llama2 coefficient of -7.5794 carrying a standard error of 1.07 $\times$ 10^{4} — standard errors between one and five thousand times the size of the coefficients they attach to. That ratio is not statistical noise; it is the textbook signature of quasi-complete separation, the condition in which a logistic regression’s maximum-likelihood estimate fails to converge because some category has too few outcomes on one side of the split for the model to be identified at all. The authors’ own conclusion, stated plainly in the same section, is admirably honest about the ambiguity: “This could be either due to our limited sample size or show that these factors are not predictive of the SPE in LLMs” [ 1 ] . That sentence is doing more work than it appears to. A regression that cannot converge cannot rule an architectural account out; at best it fails to rule it in, on a design — thirteen models from three architecturally distinct families, each contributing a single data point per dataset, with architecture family and parameter count strongly collinear by construction — that was never going to have the statistical power to separate “this doesn’t matter” from “this couldn’t be tested.” The question the appendix asks is the right one. Its own numbers show the tool it reached for could not answer it.