When you truncate Matryoshka embeddings, you get the storage benefits of low-dimensional vectors with the limited expressiveness of low-dimensional vectors. Usually, what people look for in sparse vectors is to combine the storage benefits of low-dimensional vectors with the expressiveness of high-dimensional vectors. For that, you need the non-zero dimensions to be different for different vectors.