Minimal subspace search (rank-sweep probing)

Sweeps candidate subspace dimension d, jointly training a rank-d linear projection plus classifier at each d, to find the smallest d at which held-out accuracy is within a fixed tolerance of the unconstrained ceiling — operationalizes 'how many dimensions does this concept need', distinct from simply asking whether it is decodable at all.