True sparsity would imply keeping different important vector bases for different documents, but MRL doesn't magically shuffle vector bases around depending on what's your document contains, were that the case cosine similarity between the resulting documents embeddings would simply make no sense as a similarity measure.

    embedding search (0.4)
    lexical/keyword search (0.4)
    fuzzy search (0.2)

Let H(z) = (z- exp(i theta)) (z - exp(i phi))

H(z) is zero only for our two points. We'll now take the norm^2, to get something that's minimized on only our two chosen points.

|H(exp(i x))|^2 = H(z) x conj(H(z)) = sum from -2 to 2 of c_j x exp(i j x)

For some c_j (just multiply it out). Note that this is just a Fourier series with highest harmonic 2 (or k in general), so in fact our c_j tell us the four coefficients to use.

For a simpler, but numerically nastier construction, instead use (t, t^2, t^3, ...), the real moment curve. Then given k points, take the degree k poly with those zeros. Square it, negate, and we get a degree 2k polynomial that's maximized only on our selected k points. The coefficients of this poly are the coefficients of our query vector, as once expanded onto the moment curve we can evaluate polynomials by dot product with the coefficients.

The complex version is exactly the same idea but with z^k and z ranging on the unit circle instead.

As an example, current AI is famously very poor at learning relationships between non-scalar types, like complex geometry, which humans learn with ease. That isn’t too surprising because the same representation problem exists in non-AI computer science.