{"total":15,"items":[{"citing_arxiv_id":"2605.21253","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference","primary_cat":"stat.ML","submitted_at":"2026-05-20T14:41:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Derives Wasserstein bounds and explicit hyperparameter tuning rules for annealed Langevin dynamics in compositional score-based SBI, proving Linhart et al. (2026) allows larger steps and fewer total steps than Geffner et al. (2023) in the Gaussian case.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20539","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI","primary_cat":"cs.LG","submitted_at":"2026-05-19T22:22:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"OpenSeisML curates public seismic surveys into a reproducible dataset for training generative models that produce multiple statistically consistent subsurface realizations to support uncertainty-aware seismic inversion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13551","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mixed neural posterior estimation for simulators with discrete and continuous parameters","primary_cat":"cs.LG","submitted_at":"2026-05-13T13:57:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10719","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T15:28:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Boffi, and E. Vanden- Eijnden. Probabilistic Forecasting with Stochastic Interpolants and F ¨ollmer Processes.arXiv e-prints, page arXiv:2403.13724, Mar. 2024. [7] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Sch ¨olkopf. Real-Time Gravitational Wave Science with Neural Posterior Estimation. Phys. Rev. Lett., 127(24):241103, Dec. 2021. [8] M. Deistler, J. Boelts, P. Steinbach, G. Moss, T. Moreau, M. Gloeckler, P. L. C. Rodrigues, J. Linhart, J. K. Lappalainen, B. K. Miller, P. J. Gonc ¸alves, J.-M. Lueckmann, C. Schr ¨oder, and J. H. Macke. Simulation-Based Inference: A Practical Guide.arXiv e-prints, page arXiv:2508.12939, Aug. 2025. [9] A. Delaunoy, J. Hermans, F. Rozet, A. Wehenkel, and G."},{"citing_arxiv_id":"2605.05652","ref_index":2,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference","primary_cat":"cs.LG","submitted_at":"2026-05-07T04:06:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"information, in addition to reducing marginal discrepancy between simulated and real-world data. References [1] Kyle Cranmer, Johann Brehmer, and Gilles Louppe. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48):30055-30062, 2020. doi: 10.1073/ pnas.1912789117. URLhttps://doi.org/10.1073/pnas.1912789117. [2] Michael Deistler, Jan Boelts, Peter Steinbach, Guy Moss, Thomas Moreau, Manuel Gloeckler, Pedro L. C. Rodrigues, Julia Linhart, Janne K. Lappalainen, Benjamin Kurt Miller, Pedro J. Gonçalves, Jan-Matthis Lueckmann, Cornelius Schröder, and Jakob H. Macke. Simulation- based inference: A practical guide, 2025. URLhttps://arxiv.org/abs/2508.12939. [3] George Papamakarios and Iain Murray."},{"citing_arxiv_id":"2605.08179","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data","primary_cat":"eess.SP","submitted_at":"2026-05-05T08:41:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Neural posterior estimation trained on simulated radar data enables probabilistic inference of terrain parameters from real Mars radar sounder profiles while conditioning on reference surface assumptions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"science computing low-frequency radar surface echoes for planetary radar using huygens-fresnel's principle,\"Radio Science, vol. 50, 09 2015. [22] G. Dulk, W. Erickson, R. Manning, and J.-L. Bougeret, \"Cal- ibration of low-frequency radio telescopes using the galactic background radiation,\"Astronomy & Astrophysics, vol. 365, no. 2, pp. 294-300, 2001. [23] L. Castaldo, D. M `ege, J. Gurgurewicz, R. Orosei, and G. Alberti, \"Global permittivity mapping of the martian surface from sharad,\" Earth and Planetary Science Letters, vol. 462, pp. 55-65, 2017. [24] M. S. Haynes, E. Chapin, and D. M. Schroeder, \"Geometric power fall-off in radar sounding,\"IEEE Transactions on Geo- science and Remote Sensing, vol."},{"citing_arxiv_id":"2604.20735","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health","primary_cat":"cs.LG","submitted_at":"2026-04-22T16:21:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Amortized neural posterior estimation via simulation-based inference delivers 82x faster inference than MCMC for heat exchanger fouling and leakage diagnosis while maintaining comparable accuracy on synthetic data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19919","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural Simulation-based Inference with Hierarchical Priors for Detached Eclipsing Binaries","primary_cat":"astro-ph.SR","submitted_at":"2026-04-21T18:59:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A multimodal amortized neural posterior estimator trained on realistic simulations recovers DEB parameters accurately with calibrated uncertainties on held-out tests.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"Parameter Distribution Bounds T ransformation / Notes Msum EMG (0,∞) Parameters (K= 1.1, µ= 0.7, σ= 0.25) qBeta(α= 2.5, β= 1.2) (0,1) Favors near-equal-mass systems uage Beta(α= 2.5, β= 1.8) (0,1) log 10 Age = 6.5 +u age(10.13−6.5) [Fe/H] Normal(µ=−1, σ= 0.5) (−∞,∞) Broad Galactic prior (R1 +R 2)/aBeta(α= 1.6, β= 4) (0,1) Detached constraint (Roche geometry) log10 eTruncated Normal(µ=−2, σ= 0.6) [−6,−4×10 −6] Favors near-circular orbits ωUniform [0,2π) Argument of periastron cosiUniform [0,0.99999]i= arccos(cosi); eclipse visibility enforced Teff,1, R1,logL 1 - - Interpolated from MIST grid (±5% tolerance) Teff,2, R2,logL 2 - - Interpolated from MIST grid (±5% tolerance) dEmpirical joint priord >0 Sampled jointly withE(B−V) fromGaia E(B−V) Conditional ondand sky positionE(B−V)≥0 Obtained from 3D Bayestar dust map"},{"citing_arxiv_id":"2604.12800","ref_index":82,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Spectroscopy of analogue black holes using simulation-based inference","primary_cat":"gr-qc","submitted_at":"2026-04-14T14:29:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Liu, W. M. Suen, C. Y. Tam, and K. Young, Phys. Rev. Lett.78, 2894 (1997), arXiv:gr- qc/9903031. [80] H.-P. Nollert and R. H. Price, J. Math. Phys.40, 980 8 (1999), arXiv:gr-qc/9810074. [81] K. Cranmer, J. Brehmer, and G. Louppe, Proceedings of the National Academy of Sciences117, 30055 (2020), https://www.pnas.org/doi/pdf/10.1073/pnas.1912789117. [82] M. Deistler, J. Boelts, P. Steinbach, G. Moss, T. Moreau, M. Gloeckler, P. L. C. Rodrigues, J. Lin- hart, J. K. Lappalainen, B. K. Miller, P. J. Gon¸ calves, J.-M. Lueckmann, C. Schr¨ oder, and J. H. Macke, Simulation-based inference: A practical guide (2025), arXiv:2508.12939 [stat.ML]. [83] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buo- nanno, and B."},{"citing_arxiv_id":"2604.07169","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FLUID: Flow-based Unified Inference for Dynamics","primary_cat":"stat.ML","submitted_at":"2026-04-08T14:59:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02520","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries","primary_cat":"physics.data-an","submitted_at":"2026-04-02T21:21:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"NPE delivers millisecond-scale parameter inference for Li-ion batteries that matches or exceeds Bayesian calibration accuracy while adding local sensitivity interpretability, though with higher voltage prediction errors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.18560","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Inferring the population properties of galactic binaries from LISA's stochastic foreground","primary_cat":"astro-ph.HE","submitted_at":"2026-02-20T19:00:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.23748","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios","primary_cat":"cs.LG","submitted_at":"2025-12-26T18:18:25+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.16709","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting -- III Deriving exact posteriors with dimension reduction and importance sampling","primary_cat":"astro-ph.IM","submitted_at":"2025-12-18T16:12:56+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Auto-encoder compression of X-ray spectra with multi-round neural posterior estimation and likelihood-based importance sampling yields posteriors statistically indistinguishable from nested sampling at roughly 10x speedup.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.13394","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast and Robust Simulation-Based Inference With Optimization Monte Carlo","primary_cat":"cs.LG","submitted_at":"2025-11-17T14:07:36+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}