DialPFN: Assumption-Conditioned In-Context Learning

In time-to-event studies, some individuals leave follow-up before the event occurs, leaving its timing unknown. Predictions then depend on assumptions about how event times relate to when follow-up ends, a relationship that the observed records alone generally cannot determine. Analysts therefore need to understand how their predictions change under alternative assumptions, not just obtain one answer under a fixed assumption. We introduce DialPFN, a prior-fitted network that accepts an observed dataset, an individual's features, and a stated dependence assumption, and returns an event-time distribution under that assumption. Synthetic studies provide complete target times for supervision, while the network receives only the corresponding partially observed table and a stated dependence assumption. At inference, changing this input produces a sensitivity analysis for each query without updating model weights. A frozen-backbone quantile head and reference-sample bagging produce individual predictive distributions. For the reference predictor, supplying the generating assumption reduces strong-dependence survival-curve error by 48.3 to 54.7% across five reserved simulation banks. Ordinary prediction is preserved across 17 real datasets, and a separate Melanoma evaluation supports stable individual sensitivity curves. All six training runs pass seven grouped evaluation checks. DialPFN makes individual predictions conditional on explicit assumptions, allowing analysts to examine how their conclusions change across plausible alternatives.