BibTeX

@article{2607.05252v1,
Author        = {Weichen Qin and Yufan Xie and Peihao Wang and Chia-Jui Chou and Minghui Du and Peng Xu and Ziren Luo and Yi Yang and Jingyi Yu and Bo Liang and Jiakai Zhang},
Title         = {FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation},
Eprint        = {2607.05252v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference. However, existing methods struggle with effective multimodal modeling. They often rely on brute-force fusion strategies that ignore the structural disparities between parameters and observations, thus limiting estimation fidelity. In this work, we introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation). Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction. Additionally, we propose an FK-steered sampling strategy that leverages intermediate observation likelihoods to guide the generative trajectories, effectively improving the sample quality during inference. Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC. Furthermore, in a real-world exoplanet orbital estimation task, FUSE successfully resolves complex parameter degeneracies that challenge existing methods, highlighting its potential to accelerate complex scientific discoveries in astrophysics and beyond.},
Year          = {2026},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2607.05252v1},
File          = {2607.05252v1.pdf}
}