One of the primary limitations of the spectral map framework is its reliance on timescale separation to effectively learn slow CVs. When multiple pathways coexist on similar timescales, the spectral map may struggle to distinguish between these pathways, leading to a merging of distinct transition routes in the reduced representation. This is particularly relevant in complex folding processes where intermediate or misfolded states may exist. If these states transition between folded and unfolded configurations on comparable timescales, the spectral map may not be able to resolve them adequately, resulting in a loss of information about the underlying dynamics. Consequently, the learned CVs may not fully capture the complexity of the folding landscape, potentially oversimplifying the reaction coordinate and missing critical insights into the mechanisms of folding.