What is the relationship between Random Forest and manifold learning assumptions?

Random Forest implicitly partitions data consistent with manifold structure in many cases.

If data lies on a low-dimensional manifold, RF splits tend to align with regions of high density, effectively approximating manifold segmentation. However, RF does not explicitly learn geometry, unlike manifold learning techniques such as Isomap or UMAP.