We construct a minimal task that isolates conditional learning in neural networks: a surjective map with K-fold ambiguity, resolved by a selector token z, so H(A | B) = log K while H(A | B, z) = 0. The model learns the marginal P(A | B) first, producing a plateau at exactly log K, before acquiring the full conditional in a sharp, collective transition. The plateau has a clean decomposition: height = log K (set by ambiguity), duration = f(D) (set by dataset size D, not K). Gradient noise stabilizes the marginal solution: higher learning rates monotonically slow the transition (3.6* across a 7* η range at fixed throughput), and batch-size reduction delays escape, consistent with an entropic force opposing departure from the low-gradient marginal. Internally, a selector-routing head assembles during the plateau, leading the loss transition by ~50% of the waiting time. This is the Type 2 directional asymmetry of Papadopoulos et al. [2024], measured dynamically: we track the excess risk from log K to zero and characterize what stabilizes it, what triggers its collapse, and how long it takes.
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers
NVIDIA Corporation（NVIDIA公司）；University of Oxford（牛津大学）；Donders Institute for Brain, Cognition, and Behaviour（大脑与行为研究所）；AITHYRA, Research Institute for Biomedical AI（生物医学人工智能研究所）
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；National University of Singapore（新加坡国立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It
School of Science and Technology, Champions College Prep（科学与技术学院，冠军大学预科）
