Survival In-Context: Prior-fitted In-context Learning Tabular Foundation Model for Survival Analysis

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Abstract:Survival analysis is crucial for many medical applications but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular covariates. While the prior-fitted paradigm, which relies on pretraining models on large collections of synthetic datasets, has recently facilitated tabular foundation models for classification and regression, its suitability for time-to-event modeling remains unclear. We propose a flexible survival data generation framework that defines a rich survival prior with explicit control over covariates and time-event distributions. Building on this prior, we introduce Survival In-Context (SIC), a prior-fitted in-context learning model for survival analysis that is pretrained exclusively on synthetic data. SIC produces individualized survival prediction in a single forward pass, requiring no task-specific training or hyperparameter tuning. Across a broad evaluation on real-world survival datasets, SIC achieves competitive or superior performance compared to classical and deep survival models, particularly in medium-sized data regimes, highlighting the promise of prior-fitted foundation models for survival analysis. The code will be made available upon publication.

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2603.29475 [cs.LG]

(or arXiv:2603.29475v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2603.29475

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dmitrii Seletkov [view email] [v1] Tue, 31 Mar 2026 09:22:52 UTC (7,986 KB)