High Retrieval Accuracy Does Not Imply Disentanglement: Diagnosing and Repairing Pseudo-Disentanglement in Frozen Visual Encoders

Retrieval accuracy is the default criterion for choosing a visual encoder, yet it is silent about whether content and style are actually separated: discriminable class boundaries suffice for high mAP even when the two factors are thoroughly mixed. We define this failure mode operationally—a representation is pseudo-disentangled when its retrieval mAP is high while its cross-factor leakage is not low—and make it measurable. Our metric, DScore, combines retrieval utility with two subspace-level leakage diagnostics, a kNN leakage proxy and a probe leakage rate; unlike the axis-aligned metrics (DCI, MIG, SAP), it does not assume each factor aligns with one latent dimension. Across nine frozen representations and three benchmarks, "high accuracy ≠ high disentanglement" holds throughout: the best style retriever falls to third once leakage is penalized, while a self-supervised encoder ranking eighth by style mAP ranks first. We then ask whether this entanglement can be repaired without fine-tuning the encoder, and show that it can. Cross-Nullspace Projection (CNP) is a closed-form, supervised post-processing step with two parts—a factor-specific LDA readout, and a bidirectional cross-projection that makes the two readouts approximately orthogonal. Most of its DScore gain (mean +0.15; probe leakage cut by 60–86%) comes from the readout, not the projection, but the projection is what makes the two factor vectors independently usable, lifting compositional retrieval from 0.19 to 0.45 mAP (2.4×). Across the benchmarks studied, content-style entanglement in frozen encoders is thus substantially linear-remediable rather than a deep semantic fusion.