BibTeX

@article{2601.22951v1,
Author        = {Mayank Nautiyal and Li Ju and Melker Ernfors and Klara Hagland and Ville Holma and Maximilian Werkö Söderholm and Andreas Hellander and Prashant Singh},
Title         = {OneFlowSBI: One Model, Many Queries for Simulation-Based Inference},
Eprint        = {2601.22951v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {We introduce \textit{OneFlowSBI}, a unified framework for simulation-based inference that learns a single flow-matching generative model over the joint distribution of parameters and observations. Leveraging a query-aware masking distribution during training, the same model supports multiple inference tasks, including posterior sampling, likelihood estimation, and arbitrary conditional distributions, without task-specific retraining. We evaluate \textit{OneFlowSBI} on ten benchmark inference problems and two high-dimensional real-world inverse problems across multiple simulation budgets. \textit{OneFlowSBI} is shown to deliver competitive performance against state-of-the-art generalized inference solvers and specialized posterior estimators, while enabling efficient sampling with few ODE integration steps and remaining robust under noisy and partially observed data.},
Year          = {2026},
Month         = {Jan},
Url           = {http://arxiv.org/abs/2601.22951v1},
File          = {2601.22951v1.pdf}
}