Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models

arXiv:2604.16775v1 Announce Type: cross Abstract: Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from other system and architectural choices. We evaluate how representation decisions affect downstream prediction after a shared one-epoch pretraining budget. We train 28 matched transformers on MIMIC-IV and evaluate them on 30 clinical outcomes in three experiments: (1) quantization granularity, ref