If you consider the actual latent space the full higher dimensional representation, and you take the first principle component, the other vectors are zero. Pretty sparse. No it's not a linked list sparse matrix. Don't be a pedant.
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.
