{"ID":5936986,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T15:38:11.834581458Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05252","arxiv_id":"2607.05252","title":"FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation","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.","short_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 param...","url_abs":"https://arxiv.org/abs/2607.05252","url_pdf":"https://arxiv.org/pdf/2607.05252v1","authors":"[\"Weichen Qin\",\"Yufan Xie\",\"Peihao Wang\",\"Chia-Jui Chou\",\"Minghui Du\",\"Peng Xu\",\"Ziren Luo\",\"Yi Yang\",\"Jingyi Yu\",\"Bo Liang\",\"Jiakai Zhang\"]","published":"2026-07-06T15:59:18Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}