OneFlowSBI: One Model, Many Queries for Simulation-Based Inference

We introduce $\mathit{\text{OneFlowSBI}}$, 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 $\mathit{\text{OneFlowSBI}}$ on ten benchmark inference problems and two high-dimensional real-world inverse problems across multiple simulation budgets. $\mathit{\text{OneFlowSBI}}$ 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.