Transformer models typically set the number of attention heads by convention or by grid search, which can be costly. A new arXiv preprint tests a simpler approach: estimate the intrinsic dimension of token representations without using labels, then use that estimate to decide how many heads to include.
The authors describe this as a small-scale study, meaning the evidence is limited. The abstract does not specify the exact mapping from intrinsic dimension to head count, only that the heuristic is meant to set the number in advance. Because the work is preliminary, it is not yet clear how well the rule generalises across model sizes and tasks.
Still, the idea is notable because it offers a principled alternative to brute-force tuning. If the relationship holds up, it could save compute and reduce guesswork in transformer design. For now, the main takeaway is that intrinsic dimension is worth exploring as a cheap prior for architecture choices.