mlsanityData to 14 Oct 2026

Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics

arXiv 2604.17670 ↗First posted 19 Apr 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate state-of-the-art predictive accuracy on extensive real-world datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 19 Apr 2026.

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References · 28

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16 more
  1. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
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  16. 2025metabeta -- A fast neural model for Bayesian mixed-effects regressioncited by 2

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