What does this equation mean?

Formally, an observed score s is not a property of a model $\theta$ alone. It is closer to $s = g(\theta, \mathcal{H}, T, k, \mathcal{P}, t) + \varepsilon$. a function of the weights $\theta$ , the harness $\mathcal{H}$ , the sampling temperature T , the number of samples k and how they are aggregated, the prompt template $\mathcal{P}$ , the date t a snapshot was queried, plus sampling noise $\varepsilon$ . A citation that fixes $\theta$ and leaves the other five free has not specified a comparable quantity. OpenAI’s own simple-evals table makes the same point from a different angle: rows the lab ran itself under a disclosed zero-shot chain-of-thought protocol sit beside rows for competitors’ models labelled “unknown” prompt style, sourced from those vendors’ own announcements rather than reproduced on OpenAI’s harness [ 5 ] . Even holding the harness fixed, dated snapshots of the same public model name diverge: that table lists three different GPT-4o release dates scoring 46.0%, 49.9%, and 53.1% on GPQA under the identical simple-evals protocol — a seven-point range attributable to nothing but which week the model was queried [ 5 ] .