{"total":56,"rows":[{"id":"2601.14234","title":"Q-learning with Adjoint Matching","created":"2026-01-20","cited_by_count":37,"area":"diffusion"},{"id":"2506.20701","title":"Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models","created":"2025-06-25","cited_by_count":34,"area":"diffusion"},{"id":"2602.05993","title":"Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps","created":"2026-02-05","cited_by_count":29,"area":"diffusion"},{"id":"2506.16471","title":"Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities","created":"2025-06-19","cited_by_count":26,"area":"diffusion"},{"id":"2506.03979","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","created":"2025-06-04","cited_by_count":18,"area":"diffusion"},{"id":"2506.05668","title":"RNE: plug-and-play diffusion inference-time control and energy-based training","created":"2025-06-06","cited_by_count":17,"area":"diffusion"},{"id":"2509.25170","title":"GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models","created":"2025-09-29","cited_by_count":17,"area":"diffusion"},{"id":"2511.22688","title":"Test-time scaling of diffusions with flow maps","created":"2025-11-27","cited_by_count":16,"area":"diffusion"},{"id":"2509.25171","title":"TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion","created":"2025-09-29","cited_by_count":14,"area":"diffusion"},{"id":"2510.00815","title":"Learn to Guide Your Diffusion Model","created":"2025-10-01","cited_by_count":14,"area":"diffusion"},{"id":"2506.05231","title":"Progressive Tempering Sampler with Diffusion","created":"2025-06-05","cited_by_count":13,"area":"diffusion"},{"id":"2509.25519","title":"Flow Matching with Semidiscrete Couplings","created":"2025-09-29","cited_by_count":13,"area":"diffusion"},{"id":"2602.16634","title":"Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models","created":"2026-02-18","cited_by_count":13,"area":"diffusion"},{"id":"2509.23265","title":"CREPE: Controlling Diffusion with Replica Exchange","created":"2025-09-27","cited_by_count":11,"area":"diffusion"},{"id":"2601.10403","title":"Discrete Feynman-Kac Correctors","created":"2026-01-15","cited_by_count":11,"area":"diffusion"},{"id":"2505.19367","title":"Adaptive Diffusion Guidance via Stochastic Optimal Control","created":"2025-05-25","cited_by_count":10,"area":"diffusion"},{"id":"2508.21016","title":"Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance","created":"2025-08-28","cited_by_count":10,"area":"diffusion"},{"id":"2510.01184","title":"Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models","created":"2025-10-01","cited_by_count":7,"area":"diffusion"},{"id":"2505.21101","title":"Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance","created":"2025-05-27","cited_by_count":5,"area":"diffusion"},{"id":"2605.23346","title":"Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion","created":"2026-05-22","cited_by_count":5,"area":"diffusion"},{"id":"2511.09216","title":"Controllable protein design with particle-based Feynman-Kac steering","created":"2025-11-12","cited_by_count":4,"area":"diffusion"},{"id":"2510.08462","title":"Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models","created":"2025-10-09","cited_by_count":3,"area":"diffusion"},{"id":"2510.13763","title":"PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference","created":"2025-10-15","cited_by_count":3,"area":"diffusion"},{"id":"2605.07746","title":"Flow Matching for Count Data","created":"2026-05-08","cited_by_count":3,"area":"diffusion"},{"id":"2605.18745","title":"SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate","created":"2026-05-18","cited_by_count":3,"area":"diffusion"},{"id":"2510.07343","title":"Local MAP Sampling for Diffusion Models","created":"2025-10-07","cited_by_count":2,"area":"diffusion"},{"id":"2512.05092","title":"Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction","created":"2025-12-04","cited_by_count":2,"area":"diffusion"},{"id":"2606.13565","title":"A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding","created":"2026-06-11","cited_by_count":2,"area":"diffusion"},{"id":"2607.19725","title":"Analytic Distribution of Classifier-Free Guidance for Schedule Design","created":"2026-07-22","cited_by_count":2,"area":"diffusion"},{"id":"2601.11444","title":"When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models","created":"2026-01-16","cited_by_count":1,"area":"diffusion"},{"id":"2602.05549","title":"Logical Guidance for the Exact Composition of Diffusion Models","created":"2026-02-05","cited_by_count":1,"area":"diffusion"},{"id":"2602.12229","title":"Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser","created":"2026-02-12","cited_by_count":1,"area":"diffusion"},{"id":"2603.19146","title":"D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding","created":"2026-03-19","cited_by_count":1,"area":"diffusion"},{"id":"2604.21066","title":"Optimizing Diffusion Priors in Image Reconstruction from a Single Observation","created":"2026-04-22","cited_by_count":1,"area":"diffusion"},{"id":"2606.23920","title":"Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate","created":"2026-06-22","cited_by_count":1,"area":"diffusion"},{"id":"2609.17823","title":"METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling","created":"2026-09-15","cited_by_count":1,"area":"diffusion"},{"id":"2609.26402","title":"OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery","created":"2026-09-22","cited_by_count":1,"area":"diffusion"},{"id":"2609.35947","title":"FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models","created":"2026-09-28","cited_by_count":1,"area":"diffusion"},{"id":"2603.25980","title":"A Priori Sampling of Transition States with Guided Diffusion","created":"2026-03-26","cited_by_count":0,"area":"diffusion"},{"id":"2606.08743","title":"Guided Discovery of New Behaviors using Diffusion Policies","created":"2026-06-07","cited_by_count":0,"area":"diffusion"},{"id":"2606.21014","title":"BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling","created":"2026-06-19","cited_by_count":0,"area":"diffusion"},{"id":"2607.02834","title":"On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization","created":"2026-07-03","cited_by_count":0,"area":"diffusion"},{"id":"2607.10853","title":"Diversify Diffusion with Temperature Sampling and Variance-Corrective Time Shifting","created":"2026-07-12","cited_by_count":0,"area":"diffusion"},{"id":"2607.11758","title":"From Global to Factor-Wise Expert Composition in Discrete Diffusion Models","created":"2026-07-13","cited_by_count":0,"area":"diffusion"},{"id":"2607.21606","title":"TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward","created":"2026-05-16","cited_by_count":0,"area":"diffusion"},{"id":"2608.08770","title":"A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models","created":"2026-08-09","cited_by_count":0,"area":"diffusion"},{"id":"2609.06761","title":"LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery","created":"2026-09-06","cited_by_count":0,"area":"diffusion"},{"id":"2609.36202","title":"FastGuide: Accelerating Reward Guidance for Diffusion Large Language Models","created":"2026-09-28","cited_by_count":0,"area":"diffusion"},{"id":"2610.00395","title":"Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo","created":"2026-09-30","cited_by_count":0,"area":"diffusion"},{"id":"2610.01933","title":"Error-Corrected Inference-Time Scaling for Imperfect Diffusion Models","created":"2026-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.03071","title":"Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming","created":"2026-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2610.05108","title":"Best-of-$N$ Guidance for Test-time Diffusion Alignment","created":"2026-10-04","cited_by_count":0,"area":"diffusion"},{"id":"2610.06002","title":"P3: Persistent Particle Planning for Constrained Diffusion Control","created":"2026-10-05","cited_by_count":0,"area":"diffusion"},{"id":"2610.08652","title":"Steering Diffusion Models to Rare Events with Sequential Monte Carlo","created":"2026-10-06","cited_by_count":0,"area":"diffusion"},{"id":"2610.16419","title":"Learning $\\omega$-Powered Conditional Distributions with Importance-Weighted Flow Matching","created":"2026-10-12","cited_by_count":0,"area":"diffusion"},{"id":"2610.16851","title":"Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models","created":"2026-10-12","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}