The ARC Prize Foundation ran extensive ablations on HRM for their slew of reasoning tasks and noted that the "hierarchical" part of their architecture is not much more impactful than a vanilla transformer of the same size with no extra hyperparameter tuning:

https://arcprize.org/blog/hrm-analysis#analyzing-hrms-contri...

By now, I seriously doubt any "readily interpretable" claims.

Nothing about human brain is "readily interpretable", and artificial neural networks - which, unlike brains, can be instrumented and experimented on easily - tend to resist interpretation nonetheless.

If there was an easy to reduce ML to "readily interpretable" representations, someone would have done so already. If there were architectures that perform similarly but are orders of magnitude more interpretable, they will be used, because interpretability is desirable. Instead, we get what we get.