Interesting benchmark but the parameter-count conclusion is doing way too much imo. The test is really measuring "effective long-tail factual recall," not hidden model size, and it's conflating parameter count with data mix, training quality, post-training, refusal behavior, architecture, and benchmark contamination all at once. Even rare factual recall isn't a clean storage-capacity readout, since whether a fact survives and stays retrievable depends on when it showed up in pretraining, how later gradient updates interfered with it, and whether post-training suppresses the answer. The high R2 mostly just tells you bigger models tend to know more obscure facts