InfoSculpt: Rebalancing Discriminative and Discoverable Information for Generalized Category Discovery

Generalized Category Discovery (GCD) poses a definitional tension: one representation space must simultaneously serve discrimination of known classes and discovery of unseen ones. We present evidence that a root cause of current GCD methods' failures on novel classes lies in unconstrained information allocation within the representation itself. Spectral analysis of a representative trained GCD model reveals two opposite pathologies coexisting in its novel-class features—over-compression into narrow manifolds, and under-compression that buries category cues in instance noise—while downstream remedies (debiasing heads, pseudo-label repair, decoupled branches) act at the classifier output and leave a residual gap. We then audit the allocation causally: through a conditional mutual information (CMI) variational bound, the trade-off is made an explicit knob, and under a matched-seed significance protocol, turning it toward discoverability improves New-class accuracy on 14/14 method–dataset pairs (+1.3 to +13.2 pp), significant in 13/13 held-out tests (Holm-adjusted p ≤ .008) while old-class accuracy is preserved on average, and it outperforms single-term and static-balancing alternatives. Integrated into five SOTA methods across seven benchmarks, InfoSculpt raises All accuracy by +2.3 pp on average (up to +17.2 pp) and ranks best among all compared methods on five benchmarks, at 1.18× end-to-end cost with zero inference overhead.