OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914295318577152 |
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| author | Nautiyal, Mayank Ju, Li Ernfors, Melker Hagland, Klara Holma, Ville Söderholm, Maximilian Werkö Hellander, Andreas Singh, Prashant |
| author_facet | Nautiyal, Mayank Ju, Li Ernfors, Melker Hagland, Klara Holma, Ville Söderholm, Maximilian Werkö Hellander, Andreas Singh, Prashant |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22951 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OneFlowSBI: One Model, Many Queries for Simulation-Based Inference Nautiyal, Mayank Ju, Li Ernfors, Melker Hagland, Klara Holma, Ville Söderholm, Maximilian Werkö Hellander, Andreas Singh, Prashant Machine Learning 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. |
| title | OneFlowSBI: One Model, Many Queries for Simulation-Based Inference |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.22951 |