Matryoshka Representation Learning hits structural limits

Rigid nesting forces lower dimensions to "crowd" information, leading to significant recall drops compared to dedicated small-model embeddings. Furthermore, equal loss weighting across dimensions is often sub-optimal, as recall and precision layers require different optimization pressure. Funnel retrieval architectures using MRL add significant deployment complexity, requiring empirical tuning of shortlist sizes for every use case. Finally, conflicts between different dimensional heads during training can lead to higher gradient variance and slower convergence.