{"total":20,"rows":[{"id":"2512.20685","title":"Diffusion Models in Simulation-Based Inference: A Tutorial Review","created":"2025-12-22","cited_by_count":11,"area":"diffusion"},{"id":"2608.00146","title":"DiffusionGemma Technical Report","created":"2026-07-31","cited_by_count":11,"area":"diffusion"},{"id":"2605.15741","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","created":"2026-05-15","cited_by_count":9,"area":"diffusion"},{"id":"2512.07661","title":"Optimization-Guided Diffusion for Interactive Scene Generation","created":"2025-12-08","cited_by_count":2,"area":"diffusion"},{"id":"2602.00652","title":"Solving Room Impulse Response Inverse Problems Using Flow Matching with Analytic Wiener Denoiser","created":"2026-01-31","cited_by_count":2,"area":"diffusion"},{"id":"2605.06156","title":"Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning","created":"2026-05-07","cited_by_count":2,"area":"diffusion"},{"id":"2512.23870","title":"Max-Entropy Reinforcement Learning with Flow Matching and A Case Study on LQR","created":"2025-12-29","cited_by_count":1,"area":"diffusion"},{"id":"2602.00199","title":"Reducing Memorisation in Generative Models via Riemannian Bayesian Inference","created":"2026-01-30","cited_by_count":1,"area":"diffusion"},{"id":"2604.24487","title":"Guiding Vector Field Generation via Score-based Diffusion Model","created":"2026-04-27","cited_by_count":1,"area":"diffusion"},{"id":"2605.06829","title":"A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models","created":"2026-05-07","cited_by_count":1,"area":"diffusion"},{"id":"2609.09905","title":"FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models","created":"2026-09-09","cited_by_count":1,"area":"diffusion"},{"id":"2510.01031","title":"Secure and reversible face anonymization with diffusion models","created":"2025-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2601.20363","title":"Can Continuous-Time Diffusion Models Generate and Solve Globally Constrained Discrete Problems? A Study on Sudoku","created":"2026-01-28","cited_by_count":0,"area":"diffusion"},{"id":"2604.15009","title":"Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching","created":"2026-04-16","cited_by_count":0,"area":"diffusion"},{"id":"2605.10398","title":"SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements","created":"2026-05-11","cited_by_count":0,"area":"diffusion"},{"id":"2605.22586","title":"A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models","created":"2026-05-21","cited_by_count":0,"area":"diffusion"},{"id":"2609.02186","title":"Quantum MeanFlow: single-shot generative sampling on NISQ hardware","created":"2026-09-02","cited_by_count":0,"area":"diffusion"},{"id":"2609.09009","title":"Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks","created":"2026-09-08","cited_by_count":0,"area":"diffusion"},{"id":"2609.21989","title":"Time series generation with spectrally aligned latent flow matching","created":"2026-09-18","cited_by_count":0,"area":"diffusion"},{"id":"2610.10190","title":"Universal Local Error and Realized Amplification for the First-Order EDM Predictor","created":"2026-10-07","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}