Problem
Currently, each external variable is a single-value Pedersen commitment: V = v*B + b*B_blinding. However, some applications (e.g. ML) may need to commit to whole vectors of N values and using N pedersen commitments would blow up the size of the proof needlessly.
Idea
(Warning: this is a very quick midnight draft, without much thought put into it.)

Consider the above equation. The vector v could be split in subvectors committed with orthogonal generators B_i as vector Pedersen commitments. The rest of the protocol stays the same and the change is backwards compatible with single-value commitments.
Problem
Currently, each external variable is a single-value Pedersen commitment:
V = v*B + b*B_blinding. However, some applications (e.g. ML) may need to commit to whole vectors of N values and using N pedersen commitments would blow up the size of the proof needlessly.Idea
(Warning: this is a very quick midnight draft, without much thought put into it.)
Consider the above equation. The vector v could be split in subvectors committed with orthogonal generators
B_ias vector Pedersen commitments. The rest of the protocol stays the same and the change is backwards compatible with single-value commitments.