Medical World Model: Generative Simulation of Long-Horizon Surgery From Untimed Action Descriptions

Surgical descriptions convey a surgeon's intended actions during procedures that can span hours, yet they leave open how those actions unfold and reshape the operative scene over time. Translating such descriptions into sustained visual simulation requires capturing both the progression of interacting actions and their lasting effects on tissue. We introduce SurgWAVE, a world model that generates successive surgical video blocks from an initial observation and untimed action descriptions. The model reconciles correlated descriptions without prescribing a frame-wise action schedule, retains accumulated intervention effects through utility-calibrated recurrent memory, and preserves local structure through training-only spatial distillation. To support this task, we introduce LiRAct, an expert-annotated laparoscopic liver-resection dataset and benchmark comprising 3,333 operative episodes from 18 patients. Experiments on LiRAct and Cholec80 demonstrate state-of-the-art performance in conditional surgical video generation. The code and benchmark will be released upon acceptance.