What does this equation mean?

There is a structural reason this compounds rather than merely adding up, and it is worth making explicit because it exposes an assumption rather than a vague sense of difficulty. A vision transformer front end tokenises an image of height H and width W at patch size P into $N_{\mathrm{tok}}$ = $\frac{H}{P}$ $\cdot$ $\frac{W}{P}$, and a video of T sampled frames multiplies that by T again. Any fixed context budget therefore forces a three-way trade between spatial patch size, temporal sampling rate, and clip duration — coarsen the patches, subsample frames, or truncate the clip. Whichever is chosen, information is discarded before a language model ever sees a token, and the TemporalBench failure mode — missing whether an action happened twice or three times — is exactly what temporal subsampling below the rate of the action would predict. This is not a claim that either paper makes explicitly; it is offered here as the mechanism that makes their independently reported findings consistent with each other rather than coincidental.